Recession Indicators

Introduction

For one of my machine learning classes we had a project that consumed financial data. I have extended that project to use machine learning to see if an indicator, or predictor, can be found that identifies market tops that occur prior to recessions. Then I use the model to build a trading strategy and backtest it to see how it performs.

Get Economic and Financial Data

Acquiring the data consists of two steps. First the code pulls the data into zoo objects which are then collapsed into a single data frame (df.data). Features are extracted from these series and added to the df.data data frame.

Sample call to pull economic data

Data is pulled from several sources include FRED, yahoo, and Google. The code below shows an example that pulls in the consumer price index (CPI) from the FRED. I pull data using quantmod, Quandl, and some manual extractions stored in spreadsheets.

# Consumer Price Index for All Urban Consumers: All Items
if (bRefresh == TRUE) {
  getSymbols("CPIAUCSL", src = "FRED", auto.assign = TRUE)
}

## [1] "CPIAUCSL"

## [1] "CPIAUCSL"
## [1] "USREC"
## [1] "UNRATE"
## [1] "PCEPI"
## [1] "CCSA"
## [1] "CCNSA"
## [1] "NPPTTL"
## [1] "U6RATE"
## [1] "PAYNSA"
## [1] "TABSHNO"
## [1] "HNONWPDPI"
## [1] "INDPRO"
## [1] "RRSFS"
## [1] "RSALES"
## [1] "W875RX1"
## [1] "RPI"
## [1] "PCOPPUSDM"
## [1] "NOBL"
## [1] "SCHD"
## [1] "PFF"
## [1] "HPI"
## [1] "GSFTX"
## [1] "LFMIX"
## [1] "LFMCX"
## [1] "LFMAX"
## [1] "LCSIX"
## [1] "BSV"
## [1] "VBIRX"
## [1] "BIV"
## [1] "VFSUX"
## [1] "LTUIX"
## [1] "PTTPX"
## [1] "NERYX"
## [1] "STIGX"
## [1] "HLGAX"
## [1] "FTRGX"
## [1] "THIIX"
## [1] "PTTRX"
## [1] "BFIGX"
## [1] "VTWO"
## [1] "EIFAX"
## [1] "ASDAX"

## Warning: ASDAX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## [1] "TRBUX"
## [1] "PRVIX"
## [1] "PRWCX"
## [1] "ADOZX"
## [1] "MERFX"
## [1] "CMNIX"
## [1] "CIHEX"
## [1] "IMPCH"
## [1] "EXPCH"
## [1] "IMPMX"
## [1] "EXPMX"
## [1] "HSN1FNSA"
## [1] "HNFSUSNSA"
## [1] "BUSLOANS"
## [1] "TOTCI"
## [1] "BUSLOANSNSA"
## [1] "REALLNNSA"
## [1] "REALLN"
## [1] "RELACBW027NBOG"
## [1] "RELACBW027SBOG"
## [1] "RREACBM027NBOG"
## [1] "RREACBM027SBOG"
## [1] "RREACBW027SBOG"
## [1] "RREACBW027NBOG"
## [1] "MORTGAGE30US"
## [1] "CONSUMERNSA"
## [1] "TOTLLNSA"
## [1] "DPSACBW027SBOG"
## [1] "DRCLACBS"
## [1] "TOTCINSA"
## [1] "SRPSABSNNCB"
## [1] "ASTLL"
## [1] "FBDILNECA"
## [1] "ASOLAL"
## [1] "ASTMA"
## [1] "ASHMA"
## [1] "ASMRMA"
## [1] "ASCMA"
## [1] "ASFMA"
## [1] "CCLBSHNO"
## [1] "FBDSILQ027S"
## [1] "FBLL"
## [1] "NCBDBIQ027S"
## [1] "DGS10"
## [1] "^TNX"

## Warning: ^TNX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## Warning: CL=F contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## [1] "DGS30"
## [1] "DGS1"
## [1] "DGS2"
## [1] "TB3MS"
## [1] "DTB3"
## [1] "^IRX"

## Warning: ^IRX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## [1] "DCOILWTICO"
## [1] "DCOILBRENTEU"
## [1] "NEWORDER"
## [1] "ALTSALES"
## [1] "ICSA"
## [1] "^GSPC"
## [1] "FXAIX"
## [1] "FTIHX"
## [1] "MDIZX"
## [1] "DODIX"
## [1] "^RLG"

## Warning: ^RLG contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## [1] "^DJI"
## [1] "^STOXX50E"

## Warning: ^STOXX50E contains missing values. Some functions will not work if
## objects contain missing values in the middle of the series. Consider using
## na.omit(), na.approx(), na.fill(), etc to remove or replace them.

## [1] "EFA"
## [1] "GDP"
## [1] "FNDEFX"
## [1] "FDEFX"
## [1] "GDPNOW"
## [1] "GDPC1"
## [1] "GDPDEF"
## [1] "VIG"
## [1] "WLRRAL"
## [1] "FEDFUNDS"
## [1] "GPDI"
## [1] "W790RC1Q027SBEA"
## [1] "MZMV"
## [1] "M1"
## [1] "M2"
## [1] "OPHNFB"
## [1] "IPMAN"
## [1] "IWD"
## [1] "GS5"
## [1] "PSAVERT"
## [1] "VIXCLS"
## [1] "VXX"
## [1] "HOUST1F"
## [1] "GFDEBTN"
## [1] "HOUST"
## [1] "HOUSTNSA"
## [1] "EXHOSLUSM495S"
## [1] "MSPUS"
## [1] "UMDMNO"
## [1] "DGORDER"
## [1] "CSUSHPINSA"
## [1] "GFDEGDQ188S"
## [1] "FYFSD"
## [1] "FYFSGDA188S"
## [1] "GDX"
## [1] "XLE"
## [1] "GSG"
## [1] "WALCL"
## [1] "OUTMS"
## [1] "MANEMP"
## [1] "PRS30006163"
## [1] "BAMLC0A3CA"
## [1] "AAA"
## [1] "SOFR"
## [1] "SOFRVOL"
## [1] "SOFR99"
## [1] "SOFR75"
## [1] "SOFR25"
## [1] "SOFR1"
## [1] "OBFR"
## [1] "OBFR99"
## [1] "OBFR75"
## [1] "OBFR25"
## [1] "OBFR1"
## [1] "RPONTSYD"
## [1] "IOER"
## [1] "WRESBAL"
## [1] "EXCSRESNW"
## [1] "ECBASSETS"
## [1] "EUNNGDP"
## [1] "CEU0600000007"
## [1] "CURRENCY"
## [1] "WCURRNS"
## [1] "BOGMBASE"
## [1] "PRS88003193"
## [1] "PPIACO"
## [1] "PCUOMFGOMFG"
## [1] "POPTHM"
## [1] "POPTHM"
## [1] "CLF16OV"
## [1] "LNU01000000"
## [1] "LNU03000000"
## [1] "UNEMPLOY"
## [1] "RSAFS"
## [1] "FRGSHPUSM649NCIS"
## [1] "BOPGTB"
## [1] "TERMCBPER24NS"
## [1] "A065RC1A027NBEA"
## [1] "PI"
## [1] "PCE"
## [1] "A053RC1Q027SBEA"
## [1] "CPROFIT"
## [1] "SPY"
## [1] "MDY"
## [1] "EES"
## [1] "IJR"
## [1] "VGSTX"
## [1] "VFINX"
## [1] "VOE"
## [1] "VOT"
## [1] "TMFGX"

## Warning: TMFGX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## [1] "IWM"
## [1] "ONEQ"
## [1] "FSMAX"
## [1] "FXNAX"
## [1] "HAINX"
## [1] "HNACX"
## [1] "VEU"
## [1] "VEIRX"
## [1] "BIL"
## [1] "IVOO"
## [1] "VO"
## [1] "CZA"
## [1] "VYM"
## [1] "ACWI"
## [1] "SLY"

## Warning: SLY contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## [1] "QQQ"
## [1] "HYMB"
## [1] "GOLD"
## [1] "BKR"
## [1] "SLB"
## [1] "HAL"
## [1] "IP"
## [1] "PKG"
## [1] "UPS"
## [1] "FDX"
## [1] "T"
## [1] "VZ"

Load up the EIA data

Load rig count data

The Baker Hughes rig count numbers

USDA data

Loading in farm data

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
## Expecting numeric in E3 / R3C5: got a date

## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * ...

## Warning: NAs introduced by coercion

Loading in Silverblatt’s S&P 500 spreadsheet starting with the quarterly data.

## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...5
## * `` -> ...6
## * `` -> ...7

Now load in the estimates

## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * ...

Covid 19 Data

Get the Covid-19 data from JHU

## Rows: 919308 Columns: 15
## -- Column specification ------------------------------------------------------------------------------------------------
## Delimiter: ","
## chr  (8): province, country, type, iso2, iso3, combined_key, continent_name,...
## dbl  (6): lat, long, cases, uid, code3, population
## date (1): date
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Downloading GitHub repo RamiKrispin/coronavirus@master

##   
  
  
v  checking for file 'C:\Users\Rainy\AppData\Local\Temp\RtmpiWUwmF\remotes5f107cce5034\RamiKrispin-coronavirus-b286a3c/DESCRIPTION'
## 
  
  
  
-  preparing 'coronavirus': (1.9s)
##    checking DESCRIPTION meta-information ...
  
   checking DESCRIPTION meta-information ... 
  
v  checking DESCRIPTION meta-information
## 
  
  
  
-  checking for LF line-endings in source and make files and shell scripts (532ms)
## 
  
  
  
-  checking for empty or unneeded directories
## 
  
  
  
-  building 'coronavirus_0.3.32.tar.gz'
## 
  
   
## 

## Caught an warning!

## <simpleWarning: package 'coronavirus' is in use and will not be installed>

## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.

## Warning: Removed 3 row(s) containing missing values (geom_path).

Feature Extraction

With the raw data downloaded, some of the interesting features can be extracted. The first step is reconcile the time intervals. Some of the data is released monthly and some daily. I chose to interpolate all data to a daily interval. The first section of code adds the daily rows to the dataframe.

The code performs interpolation for continuous data or carries it forward for binary data like the recession indicators.

source("calcInterpolate.r")
df.data <- calcInterpolate(df.symbols)

## Warning in merge.xts(xtsData, get(df.symbols$string.symbol[idx])): NAs
## introduced by coercion

## Warning in merge.xts(xtsData, get(df.symbols$string.symbol[idx])): NAs
## introduced by coercion

## Warning in merge.xts(xtsData, get(df.symbols$string.symbol[idx])): NAs
## introduced by coercion

Truncate data

Create aggregate series

Some analysis requires that two or more series be combined. For example, normallizing debt by GDP to get a sense of the proportion of debt to the total economy helps understand the debt cycle.

Year over year, smoothed derivative, and log trends tend to smooth out seasonal variation. It gets used so often that I do this for every series downloaded.

source("calcFeatures.r")
lst.df <- calcFeatures(df.data, df.symbols)

## [1] "USREC has zero or negative values. Log series will be zero."
## [1] "GSFTX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMIX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMCX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMAX.Volume has zero or negative values. Log series will be zero."
## [1] "LCSIX.Volume has zero or negative values. Log series will be zero."
## [1] "VBIRX.Volume has zero or negative values. Log series will be zero."
## [1] "VFSUX.Volume has zero or negative values. Log series will be zero."
## [1] "LTUIX.Volume has zero or negative values. Log series will be zero."
## [1] "PTTPX.Volume has zero or negative values. Log series will be zero."
## [1] "NERYX.Volume has zero or negative values. Log series will be zero."
## [1] "STIGX.Volume has zero or negative values. Log series will be zero."
## [1] "HLGAX.Volume has zero or negative values. Log series will be zero."
## [1] "FTRGX.Volume has zero or negative values. Log series will be zero."
## [1] "THIIX.Volume has zero or negative values. Log series will be zero."
## [1] "PTTRX.Volume has zero or negative values. Log series will be zero."
## [1] "BFIGX.Volume has zero or negative values. Log series will be zero."
## [1] "EIFAX.Volume has zero or negative values. Log series will be zero."
## [1] "ASDAX.Volume has zero or negative values. Log series will be zero."
## [1] "TRBUX.Volume has zero or negative values. Log series will be zero."
## [1] "PRVIX.Volume has zero or negative values. Log series will be zero."
## [1] "PRWCX.Volume has zero or negative values. Log series will be zero."
## [1] "ADOZX.Volume has zero or negative values. Log series will be zero."
## [1] "MERFX.Volume has zero or negative values. Log series will be zero."
## [1] "CMNIX.Volume has zero or negative values. Log series will be zero."
## [1] "CIHEX.Volume has zero or negative values. Log series will be zero."
## [1] "SRPSABSNNCB has zero or negative values. Log series will be zero."
## [1] "TNX.Volume has zero or negative values. Log series will be zero."
## [1] "CLF.Open has zero or negative values. Log series will be zero."
## [1] "CLF.Low has zero or negative values. Log series will be zero."
## [1] "CLF.Close has zero or negative values. Log series will be zero."
## [1] "CLF.Volume has zero or negative values. Log series will be zero."
## [1] "CLF.Adjusted has zero or negative values. Log series will be zero."
## [1] "DTB3 has zero or negative values. Log series will be zero."
## [1] "IRX.Open has zero or negative values. Log series will be zero."
## [1] "IRX.High has zero or negative values. Log series will be zero."
## [1] "IRX.Low has zero or negative values. Log series will be zero."
## [1] "IRX.Close has zero or negative values. Log series will be zero."
## [1] "IRX.Volume has zero or negative values. Log series will be zero."
## [1] "IRX.Adjusted has zero or negative values. Log series will be zero."
## [1] "DCOILWTICO has zero or negative values. Log series will be zero."
## [1] "FXAIX.Volume has zero or negative values. Log series will be zero."
## [1] "FTIHX.Volume has zero or negative values. Log series will be zero."
## [1] "MDIZX.Volume has zero or negative values. Log series will be zero."
## [1] "DODIX.Volume has zero or negative values. Log series will be zero."
## [1] "RLG.Volume has zero or negative values. Log series will be zero."
## [1] "STOXX50E.Volume has zero or negative values. Log series will be zero."
## [1] "GDPNOW has zero or negative values. Log series will be zero."
## [1] "W790RC1Q027SBEA has zero or negative values. Log series will be zero."
## [1] "VXX.Volume has zero or negative values. Log series will be zero."
## [1] "FYFSD has zero or negative values. Log series will be zero."
## [1] "FYFSGDA188S has zero or negative values. Log series will be zero."
## [1] "SOFR25 has zero or negative values. Log series will be zero."
## [1] "SOFR1 has zero or negative values. Log series will be zero."
## [1] "RPONTSYD has zero or negative values. Log series will be zero."
## [1] "BOPGTB has zero or negative values. Log series will be zero."
## [1] "EES.Volume has zero or negative values. Log series will be zero."
## [1] "VGSTX.Volume has zero or negative values. Log series will be zero."
## [1] "VFINX.Volume has zero or negative values. Log series will be zero."
## [1] "TMFGX.Volume has zero or negative values. Log series will be zero."
## [1] "FSMAX.Volume has zero or negative values. Log series will be zero."
## [1] "FXNAX.Volume has zero or negative values. Log series will be zero."
## [1] "HAINX.Volume has zero or negative values. Log series will be zero."
## [1] "HNACX.Volume has zero or negative values. Log series will be zero."
## [1] "VEIRX.Volume has zero or negative values. Log series will be zero."
## [1] "IVOO.Volume has zero or negative values. Log series will be zero."
## [1] "VO.Volume has zero or negative values. Log series will be zero."
## [1] "CZA.Volume has zero or negative values. Log series will be zero."
## [1] "SLY.Volume has zero or negative values. Log series will be zero."
## [1] "HYMB.Volume has zero or negative values. Log series will be zero."
## [1] "GOLD.Open has zero or negative values. Log series will be zero."
## [1] "GOLD.Volume has zero or negative values. Log series will be zero."
## [1] "BKR.Open has zero or negative values. Log series will be zero."
## [1] "BKR.Volume has zero or negative values. Log series will be zero."
## [1] "HAL.Open has zero or negative values. Log series will be zero."
## [1] "HAL.Volume has zero or negative values. Log series will be zero."
## [1] "IP.Open has zero or negative values. Log series will be zero."
## [1] "T.Open has zero or negative values. Log series will be zero."
## [1] "OPEARNINGSPERSHARE has zero or negative values. Log series will be zero."
## [1] "AREARNINGSPERSHARE has zero or negative values. Log series will be zero."
## [1] "OCCEquityVolume has zero or negative values. Log series will be zero."
## [1] "OCCNonEquityVolume has zero or negative values. Log series will be zero."
## [1] "BUSLOANS.minus.BUSLOANSNSA has zero or negative values. Log series will be zero."
## [1] "BUSLOANS.minus.BUSLOANSNSA.by.GDP has zero or negative values. Log series will be zero."
## [1] "EXPCH.minus.IMPCH has zero or negative values. Log series will be zero."
## [1] "EXPMX.minus.IMPMX has zero or negative values. Log series will be zero."
## [1] "SRPSABSNNCB.by.GDP has zero or negative values. Log series will be zero."
## [1] "DGS30TO10 has zero or negative values. Log series will be zero."
## [1] "DGS10TO1 has zero or negative values. Log series will be zero."
## [1] "DGS10TO2 has zero or negative values. Log series will be zero."
## [1] "DGS10TOTB3MS has zero or negative values. Log series will be zero."
## [1] "DGS10TODTB3 has zero or negative values. Log series will be zero."
## [1] "DCOILWTICO.by.PPIACO has zero or negative values. Log series will be zero."
## [1] "GSPC.DailySwing has zero or negative values. Log series will be zero."

df.data <- lst.df[[1]]
df.symbols <- lst.df[[2]]

Recession calculations

Summary calculations

These values are used below

Conclusion

In this worksheet a model predicting the onset of recession was built. From the model a trading rule was derived to allow backtesting. The model performed well and the trading rule backtesting showed that applying this in the post-WWII period would have resulted in an increase in returns. That is not too bad, but there are a few changes that would likely improve the model:

• Go long on short term bonds, rather than just roll out of the market. That way at least some returns would be generated during recessions.
• Refine the recession indicator.

Market Conditions

#The model is predicting a `r paste(sprintf("%3.0f", tail(df.data$recession.initiation.smooth.avg,1)[[1]]*100), "%", sep="")` chance of recession in the next 12 months. :

#- P/E ratio of `r sprintf("%3.2f", tail(df.data$MULTPLSP500PERATIOMONTH,1))` compares to a historical mean value over the last decade of `r sprintf("%3.2f", df.data$MULTPLSP500PERATIOMONTH_Mean[1])`. Since 2008 recession P/E has only fallen below historical norm a few times. The current value is high, but well off the peaks. If earnings are +2-4% year-over-year then it is not unrealistic.

As of Feb 2020 we have entered a recession as defined by the NBER yet the market continues to rise.

P/E ratio of 24.43 compares to a historical mean value over the last decade of 18.69. Since 2008 recession P/E has only fallen below historical norm a few times. The current value is high, but well off the peaks. If earnings are +2-4% year-over-year then it is not unrealistic.

• S&P 500 Volume, last updated on 2023-06-21, is flat over the last year and positive over the last month.

Unemployment

• Headline unemployment (U-3) stands at 3.70% (last updated on 2023-05-01) which is near the 1-year average of 3.57% and rising with respect to the low in the last twelve months of 3.40%. Unlikely the rate will drop again.

• Payrolls (BLS data, NSA) year-over-year stands at 2.17% which is above the 1-year average of 3.20% and falling with respect to the peak, in the last twelve months, of 4.34%.

• Jobless claims (ICSA data) year-over-year stands at 22.95% (last updated on 2023-06-17) which is in-line with the 1-year average of -12.88% and below the peak, in the last twelve months, of 22.95%.

## Warning: Removed 1 rows containing missing values (geom_text).

## Warning: Removed 1 rows containing missing values (geom_hline).

Personal Income

• Real personal income year over year growth stands at 1.05% (last updated on 2023-04-01). This is below the recent peak of 1.76%.

Yield Curve and Bond Market

• The 10-year to 3-month yield stands at -1.40% (last updated on 2023-06-20). This is above the recent low of -1.73%. The trend is positive over the last year and negative over the last month.

## Warning: Removed 1 rows containing missing values (geom_text).

## Warning: Removed 1 rows containing missing values (geom_hline).

• Auto sales flat?

Auxillary Series

I explored additional data series. The sections below have those data series along with comments.

Recent Highs

Print out the new 180 day high values

df.symbolsTrue <-
  df.symbols[df.symbols$'Max180' == TRUE, c("string.symbol", "string.description")]
df.symbolsTrue <-
  df.symbolsTrue[!(is.na(df.symbolsTrue$string.symbol)), ]
df.symbolsTrue <-
  df.symbolsTrue[!(df.symbolsTrue$string.symbol == 'USREC'), ]
#print(head(df.symbolsTrue,20))

kable(df.symbolsTrue, caption = "6-Month High") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))  

6-Month High

	string.symbol	string.description
1	CPIAUCSL	Consumer Price Index for All Urban Consumers: All Items
3	UNRATE	Civilian Unemployment Rate U-3
4	PCEPI	Personal Consumption Expenditures: Chain-type Price Index
7	NPPTTL	Total Nonfarm Private Payroll Employment (ADP)
9	PAYNSA	All Employees: Total Nonfarm Payrolls (NSA)
10	TABSHNO	Households and nonprofit organizations; total assets, Level
14	RSALES	Real Retail Sales (DISCONTINUED)
15	W875RX1	Real personal income excluding current transfer receipts
59	REALLNNSA	Real Estate Loans, All Commercial Banks (Monthly, NSA)
60	REALLN	Real Estate Loans, All Commercial Banks (Monthly, SA)
63	RREACBM027NBOG	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
64	RREACBM027SBOG	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA)
68	CONSUMERNSA	Consumer Loans, All Commercial Banks
71	DRCLACBS	Delinquency Rate on Consumer Loans, All Commercial Banks, SA
73	SRPSABSNNCB	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
74	ASTLL	All sectors; total loans; liability, Level (NSA)
75	FBDILNECA	Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
76	ASOLAL	All sectors; other loans and advances; liability, Level (NSA)
77	ASTMA	All sectors; total mortgages; asset, Level (NSA)
78	ASHMA	All sectors; home mortgages; asset, Level (NSA)
79	ASMRMA	All sectors; multifamily residential mortgages; asset, Level (NSA)
80	ASCMA	All sectors; commercial mortgages; asset, Level (NSA)
81	ASFMA	All sectors; farm mortgages; asset, Level (NSA)
83	FBDSILQ027S	Domestic financial sectors debt securities; liability, Level (NSA)
84	FBLL	Domestic financial sectors loans; liability, Level (NSA)
85	NCBDBIQ027S	Nonfinancial corporate business; debt securities; liability, Level
92	TB3MS	3-Month Treasury Bill: Secondary Market Rate (Monthly)
97	NEWORDER	Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft
99	ICSA	Initial Jobless Claims
109	GDP	Gross Domestic Product
110	FNDEFX	Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate)
111	FDEFX	Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
112	GDPNOW	Fed Atlanta GDPNow
113	GDPC1	Real Gross Domestic Product
114	GDPDEF	Gross Domestic Product: Implicit Price Deflator
117	FEDFUNDS	Effective Federal Funds Rate
120	MZMV	Velocity of MZM Money Stock
121	M1	M1 Money Stock
122	M2	M2 Money Stock
124	IPMAN	Industrial Production: Manufacturing (NAICS)
130	HOUST1F	Privately Owned Housing Starts: 1-Unit Structures
131	GFDEBTN	Federal Debt: Total Public Debt
132	HOUST	Housing Starts: Total: New Privately Owned Housing Units Started, SA
133	HOUSTNSA	Housing Starts: Total: New Privately Owned Housing Units Started, NSA
137	DGORDER	Manufacturers’ New Orders: Durable Goods (SA)
138	CSUSHPINSA	S&P/Case-Shiller U.S. National Home Price Index (NSA)
140	FYFSD	Federal Surplus or Deficit
141	FYFSGDA188S	Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
150	AAA	Moody’s Seasoned Aaa Corporate Bond Yield
156	SOFR1	Secured Overnight Financing Rate: 1st Percentile
159	OBFR75	Overnight Bank Funding Rate: 75th Percentile
161	OBFR1	Overnight Bank Funding Rate: 1st Percentile
163	IOER	Interest Rate on Excess Reserves
165	EXCSRESNW	Excess Reserves of Depository Institutions
166	ECBASSETS	Central Bank Assets for Euro Area (11-19 Countries)
167	EUNNGDP	Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
168	CEU0600000007	Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
169	CURRENCY	Currency Component of M1 (Seasonally Adjusted)
170	WCURRNS	Currency Component of M1
171	BOGMBASE	Monetary Base; Total
175	POPTHM	Population (U.S.)
176	POPTHM	Population (U.S.)
177	CLF16OV	Civilian Labor Force Level, SA
180	UNEMPLOY	Unemployment Level, seasonally adjusted
184	TERMCBPER24NS	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan
185	A065RC1A027NBEA	Personal income (NSA)
186	PI	Personal income (SA)
187	PCE	Personal Consumption Expenditures (SA)
226	ISMMANPMI	Institute of Supply Managment PMI Composite Index
227	MULTPLSP500PERATIOMONTH	S&P 500 TTM P/E
228	MULTPLSP500SALESQUARTER	S&P 500 TTM Sales (Not Inflation Adjusted)
230	MULTPLSP500DIVMONTH	S&P 500 Dividend by Month (Inflation Adjusted)
231	CHRISCMEHG1	Copper Futures, Continuous Contract #1 (HG1) (Front Month)
232	WWDIWLDISAIRGOODMTK1	Air transport, freight
234	PETA103600001M	U.S. Total Gasoline Retail Sales by Refiners, Monthly
235	PETA123600001M	U.S. Regular Gasoline Retail Sales by Refiners, Monthly
236	PETA143B00001M	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly
237	PETA133B00001M	U.S. Premium Gasoline Bulk Sales (Volume) by Refiners, Monthly
240	TOTALNGNRPUSM	Natural Gas Rotary Rigs in Operation, Monthly
241	BKRTotal	Total Rig Count
242	BKRGas	Gas Rig Count
243	BKROil	Oil Rig Count
244	FARMINCOME	Net Farm Income
245	OPEARNINGSPERSHARE	Operating Earnings per Share
247	CASHDIVIDENDSPERSHR	Cash Dividends per Share
248	SALESPERSHR	Sales per Share
249	BOOKVALPERSHR	Book value per Share
250	CAPEXPERSHR	Cap ex per Share
251	PRICE	Price
254	FINRAMarginDebt	Margin Debt
256	OCCEquityVolume	Equity Options Volume
257	OCCNonEquityVolume	Non-Equity Options Volume
269	W875RX1.by.GDP	Real Personal Income Normalized by GDP
271	PI.by.GDP	Personal Income (SA) Normalized by GDP
274	CONSUMERNSA.by.GDP	Consumer Loans Not Seasonally Adjusted divided by GDP
275	RREACBM027NBOG.by.GDP	Residental Real Estate Loans (Monthly, NSA) divided by GDP
276	RREACBM027SBOG.by.GDP	Residental Real Estate Loans (Monthly, SA) divided by GDP
280	DGORDER.by.GDP	Durable Goods (Monthly, NSA) divided by GDP
284	CONSUMERNSA.INTEREST	Consumer Loans (Not Seasonally Adjusted) Interest Burdens
285	CONSUMERNSA.INTEREST.by.GDP	Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
286	TOTLNNSA	Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA)
287	TOTLNNSA.by.GDP	Total Loans Not Seasonally Adjusted divided by GDP
296	SRPSABSNNCB.by.GDP	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP
297	ASTLL.by.GDP	All sectors; total loans; liability, Level (NSA) Divided by GDP
298	ASFMA.by.GDP	All sectors; farm mortgages; asset, Level (NSA) Divided by GDP
299	ASFMA.by.ASTLL	All sectors; total loans Divided by farm mortgages
303	BOGMBASE.by.GDP	BOGMBASE Divided by GDP
313	UNEMPLOYBYPOPTHM	Unemployment level, seasonally adjusted / Population
316	CHRISCMEHG1.by.PPIACO	Copper, $/lb, Normalized by commodities producer price index
323	GDP.by.GDPDEF	Nominal GDP Normalized by GDP def
335	MSPUS.times.HOUST	New privately owned units start times median price
336	HOUST.div.POPTHM	Housing starts divided by U.S. population
346	CPIAUCSL_Log	Log of Consumer Price Index for All Urban Consumers: All Items
347	CPIAUCSL_mva365	Consumer Price Index for All Urban Consumers: All Items 365 Day MA
348	CPIAUCSL_mva200	Consumer Price Index for All Urban Consumers: All Items 200 Day MA
349	CPIAUCSL_mva050	Consumer Price Index for All Urban Consumers: All Items 50 Day MA
350	USREC_YoY	NBER based Recession Indicators Year over Year
351	USREC_YoY4	NBER based Recession Indicators 4 Year over 4 Year
352	USREC_YoY5	NBER based Recession Indicators 5 Year over 5 Year
353	USREC_Smooth	Savitsky-Golay Smoothed (p=3, n=365) NBER based Recession Indicators
354	USREC_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) NBER based Recession Indicators
355	USREC_SmoothDer	Derivative of Smoothed NBER based Recession Indicators
356	USREC_Log	Log of NBER based Recession Indicators
357	USREC_mva365	NBER based Recession Indicators 365 Day MA
358	USREC_mva200	NBER based Recession Indicators 200 Day MA
359	USREC_mva050	NBER based Recession Indicators 50 Day MA
360	UNRATE_YoY	Civilian Unemployment Rate U-3 Year over Year
363	UNRATE_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Civilian Unemployment Rate U-3
365	UNRATE_SmoothDer	Derivative of Smoothed Civilian Unemployment Rate U-3
366	UNRATE_Log	Log of Civilian Unemployment Rate U-3
369	UNRATE_mva050	Civilian Unemployment Rate U-3 50 Day MA
376	PCEPI_Log	Log of Personal Consumption Expenditures: Chain-type Price Index
377	PCEPI_mva365	Personal Consumption Expenditures: Chain-type Price Index 365 Day MA
378	PCEPI_mva200	Personal Consumption Expenditures: Chain-type Price Index 200 Day MA
379	PCEPI_mva050	Personal Consumption Expenditures: Chain-type Price Index 50 Day MA
381	CCSA_YoY4	Continued Claims (Insured Unemployment) 4 Year over 4 Year
382	CCSA_YoY5	Continued Claims (Insured Unemployment) 5 Year over 5 Year
387	CCSA_mva365	Continued Claims (Insured Unemployment) 365 Day MA
388	CCSA_mva200	Continued Claims (Insured Unemployment) 200 Day MA
397	CCNSA_mva365	Continued Claims (Insured Unemployment, NSA) 365 Day MA
398	CCNSA_mva200	Continued Claims (Insured Unemployment, NSA) 200 Day MA
405	NPPTTL_SmoothDer	Derivative of Smoothed Total Nonfarm Private Payroll Employment (ADP)
406	NPPTTL_Log	Log of Total Nonfarm Private Payroll Employment (ADP)
407	NPPTTL_mva365	Total Nonfarm Private Payroll Employment (ADP) 365 Day MA
408	NPPTTL_mva200	Total Nonfarm Private Payroll Employment (ADP) 200 Day MA
409	NPPTTL_mva050	Total Nonfarm Private Payroll Employment (ADP) 50 Day MA
411	U6RATE_YoY4	Total unemployed + margin + part-time U-6 4 Year over 4 Year
412	U6RATE_YoY5	Total unemployed + margin + part-time U-6 5 Year over 5 Year
413	U6RATE_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Total unemployed + margin + part-time U-6
415	U6RATE_SmoothDer	Derivative of Smoothed Total unemployed + margin + part-time U-6
423	PAYNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All Employees: Total Nonfarm Payrolls (NSA)
425	PAYNSA_SmoothDer	Derivative of Smoothed All Employees: Total Nonfarm Payrolls (NSA)
426	PAYNSA_Log	Log of All Employees: Total Nonfarm Payrolls (NSA)
427	PAYNSA_mva365	All Employees: Total Nonfarm Payrolls (NSA) 365 Day MA
428	PAYNSA_mva200	All Employees: Total Nonfarm Payrolls (NSA) 200 Day MA
429	PAYNSA_mva050	All Employees: Total Nonfarm Payrolls (NSA) 50 Day MA
430	TABSHNO_YoY	Households and nonprofit organizations; total assets, Level Year over Year
436	TABSHNO_Log	Log of Households and nonprofit organizations; total assets, Level
437	TABSHNO_mva365	Households and nonprofit organizations; total assets, Level 365 Day MA
438	TABSHNO_mva200	Households and nonprofit organizations; total assets, Level 200 Day MA
439	TABSHNO_mva050	Households and nonprofit organizations; total assets, Level 50 Day MA
440	HNONWPDPI_YoY	Household Net Worth, percent Dispsable Income Year over Year
453	INDPRO_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Industrial Production Index
455	INDPRO_SmoothDer	Derivative of Smoothed Industrial Production Index
457	INDPRO_mva365	Industrial Production Index 365 Day MA
465	RRSFS_SmoothDer	Derivative of Smoothed Real Retail and Food Services Sales
470	RSALES_YoY	Real Retail Sales (DISCONTINUED) Year over Year
471	RSALES_YoY4	Real Retail Sales (DISCONTINUED) 4 Year over 4 Year
472	RSALES_YoY5	Real Retail Sales (DISCONTINUED) 5 Year over 5 Year
476	RSALES_Log	Log of Real Retail Sales (DISCONTINUED)
477	RSALES_mva365	Real Retail Sales (DISCONTINUED) 365 Day MA
478	RSALES_mva200	Real Retail Sales (DISCONTINUED) 200 Day MA
479	RSALES_mva050	Real Retail Sales (DISCONTINUED) 50 Day MA
483	W875RX1_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real personal income excluding current transfer receipts
486	W875RX1_Log	Log of Real personal income excluding current transfer receipts
487	W875RX1_mva365	Real personal income excluding current transfer receipts 365 Day MA
488	W875RX1_mva200	Real personal income excluding current transfer receipts 200 Day MA
489	W875RX1_mva050	Real personal income excluding current transfer receipts 50 Day MA
493	RPI_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real personal income
497	RPI_mva365	Real personal income 365 Day MA
498	RPI_mva200	Real personal income 200 Day MA
500	PCOPPUSDM_YoY	Global price of Copper Year over Year
555	NOBL.Volume_SmoothDer	Derivative of Smoothed
567	NOBL.Adjusted_mva365	365 Day MA
790	GSFTX.Volume_YoY	Year over Year
791	GSFTX.Volume_YoY4	4 Year over 4 Year
792	GSFTX.Volume_YoY5	5 Year over 5 Year
793	GSFTX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
794	GSFTX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
795	GSFTX.Volume_SmoothDer	Derivative of Smoothed
796	GSFTX.Volume_Log	Log of
797	GSFTX.Volume_mva365	365 Day MA
798	GSFTX.Volume_mva200	200 Day MA
799	GSFTX.Volume_mva050	50 Day MA
815	LFMIX.Open_SmoothDer	Derivative of Smoothed
825	LFMIX.High_SmoothDer	Derivative of Smoothed
835	LFMIX.Low_SmoothDer	Derivative of Smoothed
845	LFMIX.Close_SmoothDer	Derivative of Smoothed
850	LFMIX.Volume_YoY	Year over Year
851	LFMIX.Volume_YoY4	4 Year over 4 Year
852	LFMIX.Volume_YoY5	5 Year over 5 Year
853	LFMIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
854	LFMIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
855	LFMIX.Volume_SmoothDer	Derivative of Smoothed
856	LFMIX.Volume_Log	Log of
857	LFMIX.Volume_mva365	365 Day MA
858	LFMIX.Volume_mva200	200 Day MA
859	LFMIX.Volume_mva050	50 Day MA
865	LFMIX.Adjusted_SmoothDer	Derivative of Smoothed
875	LFMCX.Open_SmoothDer	Derivative of Smoothed
885	LFMCX.High_SmoothDer	Derivative of Smoothed
895	LFMCX.Low_SmoothDer	Derivative of Smoothed
905	LFMCX.Close_SmoothDer	Derivative of Smoothed
910	LFMCX.Volume_YoY	Year over Year
911	LFMCX.Volume_YoY4	4 Year over 4 Year
912	LFMCX.Volume_YoY5	5 Year over 5 Year
913	LFMCX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
914	LFMCX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
915	LFMCX.Volume_SmoothDer	Derivative of Smoothed
916	LFMCX.Volume_Log	Log of
917	LFMCX.Volume_mva365	365 Day MA
918	LFMCX.Volume_mva200	200 Day MA
919	LFMCX.Volume_mva050	50 Day MA
925	LFMCX.Adjusted_SmoothDer	Derivative of Smoothed
935	LFMAX.Open_SmoothDer	Derivative of Smoothed
945	LFMAX.High_SmoothDer	Derivative of Smoothed
955	LFMAX.Low_SmoothDer	Derivative of Smoothed
965	LFMAX.Close_SmoothDer	Derivative of Smoothed
970	LFMAX.Volume_YoY	Year over Year
971	LFMAX.Volume_YoY4	4 Year over 4 Year
972	LFMAX.Volume_YoY5	5 Year over 5 Year
973	LFMAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
974	LFMAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
975	LFMAX.Volume_SmoothDer	Derivative of Smoothed
976	LFMAX.Volume_Log	Log of
977	LFMAX.Volume_mva365	365 Day MA
978	LFMAX.Volume_mva200	200 Day MA
979	LFMAX.Volume_mva050	50 Day MA
985	LFMAX.Adjusted_SmoothDer	Derivative of Smoothed
995	LCSIX.Open_SmoothDer	Derivative of Smoothed
1005	LCSIX.High_SmoothDer	Derivative of Smoothed
1015	LCSIX.Low_SmoothDer	Derivative of Smoothed
1025	LCSIX.Close_SmoothDer	Derivative of Smoothed
1030	LCSIX.Volume_YoY	Year over Year
1031	LCSIX.Volume_YoY4	4 Year over 4 Year
1032	LCSIX.Volume_YoY5	5 Year over 5 Year
1033	LCSIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1034	LCSIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1035	LCSIX.Volume_SmoothDer	Derivative of Smoothed
1036	LCSIX.Volume_Log	Log of
1037	LCSIX.Volume_mva365	365 Day MA
1038	LCSIX.Volume_mva200	200 Day MA
1039	LCSIX.Volume_mva050	50 Day MA
1055	BSV.Open_SmoothDer	Derivative of Smoothed
1058	BSV.Open_mva200	200 Day MA
1065	BSV.High_SmoothDer	Derivative of Smoothed
1068	BSV.High_mva200	200 Day MA
1075	BSV.Low_SmoothDer	Derivative of Smoothed
1078	BSV.Low_mva200	200 Day MA
1085	BSV.Close_SmoothDer	Derivative of Smoothed
1088	BSV.Close_mva200	200 Day MA
1095	BSV.Volume_SmoothDer	Derivative of Smoothed
1105	BSV.Adjusted_SmoothDer	Derivative of Smoothed
1108	BSV.Adjusted_mva200	200 Day MA
1115	VBIRX.Open_SmoothDer	Derivative of Smoothed
1118	VBIRX.Open_mva200	200 Day MA
1125	VBIRX.High_SmoothDer	Derivative of Smoothed
1128	VBIRX.High_mva200	200 Day MA
1135	VBIRX.Low_SmoothDer	Derivative of Smoothed
1138	VBIRX.Low_mva200	200 Day MA
1145	VBIRX.Close_SmoothDer	Derivative of Smoothed
1148	VBIRX.Close_mva200	200 Day MA
1150	VBIRX.Volume_YoY	Year over Year
1151	VBIRX.Volume_YoY4	4 Year over 4 Year
1152	VBIRX.Volume_YoY5	5 Year over 5 Year
1153	VBIRX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1154	VBIRX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1155	VBIRX.Volume_SmoothDer	Derivative of Smoothed
1156	VBIRX.Volume_Log	Log of
1157	VBIRX.Volume_mva365	365 Day MA
1158	VBIRX.Volume_mva200	200 Day MA
1159	VBIRX.Volume_mva050	50 Day MA
1165	VBIRX.Adjusted_SmoothDer	Derivative of Smoothed
1168	VBIRX.Adjusted_mva200	200 Day MA
1228	BIV.Adjusted_mva200	200 Day MA
1238	VFSUX.Open_mva200	200 Day MA
1248	VFSUX.High_mva200	200 Day MA
1258	VFSUX.Low_mva200	200 Day MA
1268	VFSUX.Close_mva200	200 Day MA
1270	VFSUX.Volume_YoY	Year over Year
1271	VFSUX.Volume_YoY4	4 Year over 4 Year
1272	VFSUX.Volume_YoY5	5 Year over 5 Year
1273	VFSUX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1274	VFSUX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1275	VFSUX.Volume_SmoothDer	Derivative of Smoothed
1276	VFSUX.Volume_Log	Log of
1277	VFSUX.Volume_mva365	365 Day MA
1278	VFSUX.Volume_mva200	200 Day MA
1279	VFSUX.Volume_mva050	50 Day MA
1288	VFSUX.Adjusted_mva200	200 Day MA
1295	LTUIX.Open_SmoothDer	Derivative of Smoothed
1305	LTUIX.High_SmoothDer	Derivative of Smoothed
1315	LTUIX.Low_SmoothDer	Derivative of Smoothed
1325	LTUIX.Close_SmoothDer	Derivative of Smoothed
1330	LTUIX.Volume_YoY	Year over Year
1331	LTUIX.Volume_YoY4	4 Year over 4 Year
1332	LTUIX.Volume_YoY5	5 Year over 5 Year
1333	LTUIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1334	LTUIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1335	LTUIX.Volume_SmoothDer	Derivative of Smoothed
1336	LTUIX.Volume_Log	Log of
1337	LTUIX.Volume_mva365	365 Day MA
1338	LTUIX.Volume_mva200	200 Day MA
1339	LTUIX.Volume_mva050	50 Day MA
1345	LTUIX.Adjusted_SmoothDer	Derivative of Smoothed
1348	LTUIX.Adjusted_mva200	200 Day MA
1390	PTTPX.Volume_YoY	Year over Year
1391	PTTPX.Volume_YoY4	4 Year over 4 Year
1392	PTTPX.Volume_YoY5	5 Year over 5 Year
1393	PTTPX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1394	PTTPX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1395	PTTPX.Volume_SmoothDer	Derivative of Smoothed
1396	PTTPX.Volume_Log	Log of
1397	PTTPX.Volume_mva365	365 Day MA
1398	PTTPX.Volume_mva200	200 Day MA
1399	PTTPX.Volume_mva050	50 Day MA
1408	PTTPX.Adjusted_mva200	200 Day MA
1450	NERYX.Volume_YoY	Year over Year
1451	NERYX.Volume_YoY4	4 Year over 4 Year
1452	NERYX.Volume_YoY5	5 Year over 5 Year
1453	NERYX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1454	NERYX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1455	NERYX.Volume_SmoothDer	Derivative of Smoothed
1456	NERYX.Volume_Log	Log of
1457	NERYX.Volume_mva365	365 Day MA
1458	NERYX.Volume_mva200	200 Day MA
1459	NERYX.Volume_mva050	50 Day MA
1468	NERYX.Adjusted_mva200	200 Day MA
1510	STIGX.Volume_YoY	Year over Year
1511	STIGX.Volume_YoY4	4 Year over 4 Year
1512	STIGX.Volume_YoY5	5 Year over 5 Year
1513	STIGX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1514	STIGX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1515	STIGX.Volume_SmoothDer	Derivative of Smoothed
1516	STIGX.Volume_Log	Log of
1517	STIGX.Volume_mva365	365 Day MA
1518	STIGX.Volume_mva200	200 Day MA
1519	STIGX.Volume_mva050	50 Day MA
1570	HLGAX.Volume_YoY	Year over Year
1571	HLGAX.Volume_YoY4	4 Year over 4 Year
1572	HLGAX.Volume_YoY5	5 Year over 5 Year
1573	HLGAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1574	HLGAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1575	HLGAX.Volume_SmoothDer	Derivative of Smoothed
1576	HLGAX.Volume_Log	Log of
1577	HLGAX.Volume_mva365	365 Day MA
1578	HLGAX.Volume_mva200	200 Day MA
1579	HLGAX.Volume_mva050	50 Day MA
1588	HLGAX.Adjusted_mva200	200 Day MA
1595	FTRGX.Open_SmoothDer	Derivative of Smoothed
1605	FTRGX.High_SmoothDer	Derivative of Smoothed
1615	FTRGX.Low_SmoothDer	Derivative of Smoothed
1625	FTRGX.Close_SmoothDer	Derivative of Smoothed
1630	FTRGX.Volume_YoY	Year over Year
1631	FTRGX.Volume_YoY4	4 Year over 4 Year
1632	FTRGX.Volume_YoY5	5 Year over 5 Year
1633	FTRGX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1634	FTRGX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1635	FTRGX.Volume_SmoothDer	Derivative of Smoothed
1636	FTRGX.Volume_Log	Log of
1637	FTRGX.Volume_mva365	365 Day MA
1638	FTRGX.Volume_mva200	200 Day MA
1639	FTRGX.Volume_mva050	50 Day MA
1650	THIIX.Open_YoY	Year over Year
1658	THIIX.Open_mva200	200 Day MA
1660	THIIX.High_YoY	Year over Year
1668	THIIX.High_mva200	200 Day MA
1670	THIIX.Low_YoY	Year over Year
1678	THIIX.Low_mva200	200 Day MA
1680	THIIX.Close_YoY	Year over Year
1688	THIIX.Close_mva200	200 Day MA
1690	THIIX.Volume_YoY	Year over Year
1691	THIIX.Volume_YoY4	4 Year over 4 Year
1692	THIIX.Volume_YoY5	5 Year over 5 Year
1693	THIIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1694	THIIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1695	THIIX.Volume_SmoothDer	Derivative of Smoothed
1696	THIIX.Volume_Log	Log of
1697	THIIX.Volume_mva365	365 Day MA
1698	THIIX.Volume_mva200	200 Day MA
1699	THIIX.Volume_mva050	50 Day MA
1700	THIIX.Adjusted_YoY	Year over Year
1708	THIIX.Adjusted_mva200	200 Day MA
1750	PTTRX.Volume_YoY	Year over Year
1751	PTTRX.Volume_YoY4	4 Year over 4 Year
1752	PTTRX.Volume_YoY5	5 Year over 5 Year
1753	PTTRX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1754	PTTRX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1755	PTTRX.Volume_SmoothDer	Derivative of Smoothed
1756	PTTRX.Volume_Log	Log of
1757	PTTRX.Volume_mva365	365 Day MA
1758	PTTRX.Volume_mva200	200 Day MA
1759	PTTRX.Volume_mva050	50 Day MA
1768	PTTRX.Adjusted_mva200	200 Day MA
1775	BFIGX.Open_SmoothDer	Derivative of Smoothed
1785	BFIGX.High_SmoothDer	Derivative of Smoothed
1795	BFIGX.Low_SmoothDer	Derivative of Smoothed
1805	BFIGX.Close_SmoothDer	Derivative of Smoothed
1810	BFIGX.Volume_YoY	Year over Year
1811	BFIGX.Volume_YoY4	4 Year over 4 Year
1812	BFIGX.Volume_YoY5	5 Year over 5 Year
1813	BFIGX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1814	BFIGX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1815	BFIGX.Volume_SmoothDer	Derivative of Smoothed
1816	BFIGX.Volume_Log	Log of
1817	BFIGX.Volume_mva365	365 Day MA
1818	BFIGX.Volume_mva200	200 Day MA
1819	BFIGX.Volume_mva050	50 Day MA
1825	BFIGX.Adjusted_SmoothDer	Derivative of Smoothed
1873	VTWO.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1898	EIFAX.Open_mva200	200 Day MA
1908	EIFAX.High_mva200	200 Day MA
1918	EIFAX.Low_mva200	200 Day MA
1928	EIFAX.Close_mva200	200 Day MA
1930	EIFAX.Volume_YoY	Year over Year
1931	EIFAX.Volume_YoY4	4 Year over 4 Year
1932	EIFAX.Volume_YoY5	5 Year over 5 Year
1933	EIFAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1934	EIFAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1935	EIFAX.Volume_SmoothDer	Derivative of Smoothed
1936	EIFAX.Volume_Log	Log of
1937	EIFAX.Volume_mva365	365 Day MA
1938	EIFAX.Volume_mva200	200 Day MA
1939	EIFAX.Volume_mva050	50 Day MA
1947	EIFAX.Adjusted_mva365	365 Day MA
1948	EIFAX.Adjusted_mva200	200 Day MA
1950	ASDAX.Open_YoY	Year over Year
1958	ASDAX.Open_mva200	200 Day MA
1960	ASDAX.High_YoY	Year over Year
1968	ASDAX.High_mva200	200 Day MA
1970	ASDAX.Low_YoY	Year over Year
1978	ASDAX.Low_mva200	200 Day MA
1980	ASDAX.Close_YoY	Year over Year
1988	ASDAX.Close_mva200	200 Day MA
1990	ASDAX.Volume_YoY	Year over Year
1991	ASDAX.Volume_YoY4	4 Year over 4 Year
1992	ASDAX.Volume_YoY5	5 Year over 5 Year
1993	ASDAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1994	ASDAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1995	ASDAX.Volume_SmoothDer	Derivative of Smoothed
1996	ASDAX.Volume_Log	Log of
1997	ASDAX.Volume_mva365	365 Day MA
1998	ASDAX.Volume_mva200	200 Day MA
1999	ASDAX.Volume_mva050	50 Day MA
2000	ASDAX.Adjusted_YoY	Year over Year
2004	ASDAX.Adjusted_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2007	ASDAX.Adjusted_mva365	365 Day MA
2008	ASDAX.Adjusted_mva200	200 Day MA
2010	TRBUX.Open_YoY	Year over Year
2012	TRBUX.Open_YoY5	5 Year over 5 Year
2013	TRBUX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2014	TRBUX.Open_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2015	TRBUX.Open_SmoothDer	Derivative of Smoothed
2016	TRBUX.Open_Log	Log of
2018	TRBUX.Open_mva200	200 Day MA
2019	TRBUX.Open_mva050	50 Day MA
2020	TRBUX.High_YoY	Year over Year
2022	TRBUX.High_YoY5	5 Year over 5 Year
2023	TRBUX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2024	TRBUX.High_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2025	TRBUX.High_SmoothDer	Derivative of Smoothed
2026	TRBUX.High_Log	Log of
2028	TRBUX.High_mva200	200 Day MA
2029	TRBUX.High_mva050	50 Day MA
2030	TRBUX.Low_YoY	Year over Year
2032	TRBUX.Low_YoY5	5 Year over 5 Year
2033	TRBUX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2034	TRBUX.Low_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2035	TRBUX.Low_SmoothDer	Derivative of Smoothed
2036	TRBUX.Low_Log	Log of
2038	TRBUX.Low_mva200	200 Day MA
2039	TRBUX.Low_mva050	50 Day MA
2040	TRBUX.Close_YoY	Year over Year
2042	TRBUX.Close_YoY5	5 Year over 5 Year
2043	TRBUX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2044	TRBUX.Close_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2045	TRBUX.Close_SmoothDer	Derivative of Smoothed
2046	TRBUX.Close_Log	Log of
2048	TRBUX.Close_mva200	200 Day MA
2049	TRBUX.Close_mva050	50 Day MA
2050	TRBUX.Volume_YoY	Year over Year
2051	TRBUX.Volume_YoY4	4 Year over 4 Year
2052	TRBUX.Volume_YoY5	5 Year over 5 Year
2053	TRBUX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2054	TRBUX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2055	TRBUX.Volume_SmoothDer	Derivative of Smoothed
2056	TRBUX.Volume_Log	Log of
2057	TRBUX.Volume_mva365	365 Day MA
2058	TRBUX.Volume_mva200	200 Day MA
2059	TRBUX.Volume_mva050	50 Day MA
2060	TRBUX.Adjusted_YoY	Year over Year
2064	TRBUX.Adjusted_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2066	TRBUX.Adjusted_Log	Log of
2067	TRBUX.Adjusted_mva365	365 Day MA
2068	TRBUX.Adjusted_mva200	200 Day MA
2069	TRBUX.Adjusted_mva050	50 Day MA
2110	PRVIX.Volume_YoY	Year over Year
2111	PRVIX.Volume_YoY4	4 Year over 4 Year
2112	PRVIX.Volume_YoY5	5 Year over 5 Year
2113	PRVIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2114	PRVIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2115	PRVIX.Volume_SmoothDer	Derivative of Smoothed
2116	PRVIX.Volume_Log	Log of
2117	PRVIX.Volume_mva365	365 Day MA
2118	PRVIX.Volume_mva200	200 Day MA
2119	PRVIX.Volume_mva050	50 Day MA
2133	PRWCX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2135	PRWCX.Open_SmoothDer	Derivative of Smoothed
2143	PRWCX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2145	PRWCX.High_SmoothDer	Derivative of Smoothed
2153	PRWCX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2155	PRWCX.Low_SmoothDer	Derivative of Smoothed
2163	PRWCX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2165	PRWCX.Close_SmoothDer	Derivative of Smoothed
2170	PRWCX.Volume_YoY	Year over Year
2171	PRWCX.Volume_YoY4	4 Year over 4 Year
2172	PRWCX.Volume_YoY5	5 Year over 5 Year
2173	PRWCX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2174	PRWCX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2175	PRWCX.Volume_SmoothDer	Derivative of Smoothed
2176	PRWCX.Volume_Log	Log of
2177	PRWCX.Volume_mva365	365 Day MA
2178	PRWCX.Volume_mva200	200 Day MA
2179	PRWCX.Volume_mva050	50 Day MA
2183	PRWCX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2185	PRWCX.Adjusted_SmoothDer	Derivative of Smoothed
2188	PRWCX.Adjusted_mva200	200 Day MA
2189	PRWCX.Adjusted_mva050	50 Day MA
2193	ADOZX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2198	ADOZX.Open_mva200	200 Day MA
2199	ADOZX.Open_mva050	50 Day MA
2203	ADOZX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2208	ADOZX.High_mva200	200 Day MA
2209	ADOZX.High_mva050	50 Day MA
2213	ADOZX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2218	ADOZX.Low_mva200	200 Day MA
2219	ADOZX.Low_mva050	50 Day MA
2223	ADOZX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2228	ADOZX.Close_mva200	200 Day MA
2229	ADOZX.Close_mva050	50 Day MA
2230	ADOZX.Volume_YoY	Year over Year
2231	ADOZX.Volume_YoY4	4 Year over 4 Year
2232	ADOZX.Volume_YoY5	5 Year over 5 Year
2233	ADOZX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2234	ADOZX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2235	ADOZX.Volume_SmoothDer	Derivative of Smoothed
2236	ADOZX.Volume_Log	Log of
2237	ADOZX.Volume_mva365	365 Day MA
2238	ADOZX.Volume_mva200	200 Day MA
2239	ADOZX.Volume_mva050	50 Day MA
2243	ADOZX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2248	ADOZX.Adjusted_mva200	200 Day MA
2249	ADOZX.Adjusted_mva050	50 Day MA
2290	MERFX.Volume_YoY	Year over Year
2291	MERFX.Volume_YoY4	4 Year over 4 Year
2292	MERFX.Volume_YoY5	5 Year over 5 Year
2293	MERFX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2294	MERFX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2295	MERFX.Volume_SmoothDer	Derivative of Smoothed
2296	MERFX.Volume_Log	Log of
2297	MERFX.Volume_mva365	365 Day MA
2298	MERFX.Volume_mva200	200 Day MA
2299	MERFX.Volume_mva050	50 Day MA
2307	MERFX.Adjusted_mva365	365 Day MA
2313	CMNIX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2318	CMNIX.Open_mva200	200 Day MA
2319	CMNIX.Open_mva050	50 Day MA
2323	CMNIX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2328	CMNIX.High_mva200	200 Day MA
2329	CMNIX.High_mva050	50 Day MA
2333	CMNIX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2338	CMNIX.Low_mva200	200 Day MA
2339	CMNIX.Low_mva050	50 Day MA
2343	CMNIX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2348	CMNIX.Close_mva200	200 Day MA
2349	CMNIX.Close_mva050	50 Day MA
2350	CMNIX.Volume_YoY	Year over Year
2351	CMNIX.Volume_YoY4	4 Year over 4 Year
2352	CMNIX.Volume_YoY5	5 Year over 5 Year
2353	CMNIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2354	CMNIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2355	CMNIX.Volume_SmoothDer	Derivative of Smoothed
2356	CMNIX.Volume_Log	Log of
2357	CMNIX.Volume_mva365	365 Day MA
2358	CMNIX.Volume_mva200	200 Day MA
2359	CMNIX.Volume_mva050	50 Day MA
2363	CMNIX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2367	CMNIX.Adjusted_mva365	365 Day MA
2368	CMNIX.Adjusted_mva200	200 Day MA
2369	CMNIX.Adjusted_mva050	50 Day MA
2373	CIHEX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2375	CIHEX.Open_SmoothDer	Derivative of Smoothed
2378	CIHEX.Open_mva200	200 Day MA
2379	CIHEX.Open_mva050	50 Day MA
2383	CIHEX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2385	CIHEX.High_SmoothDer	Derivative of Smoothed
2388	CIHEX.High_mva200	200 Day MA
2389	CIHEX.High_mva050	50 Day MA
2393	CIHEX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2395	CIHEX.Low_SmoothDer	Derivative of Smoothed
2398	CIHEX.Low_mva200	200 Day MA
2399	CIHEX.Low_mva050	50 Day MA
2403	CIHEX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2405	CIHEX.Close_SmoothDer	Derivative of Smoothed
2408	CIHEX.Close_mva200	200 Day MA
2409	CIHEX.Close_mva050	50 Day MA
2410	CIHEX.Volume_YoY	Year over Year
2411	CIHEX.Volume_YoY4	4 Year over 4 Year
2412	CIHEX.Volume_YoY5	5 Year over 5 Year
2413	CIHEX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2414	CIHEX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2415	CIHEX.Volume_SmoothDer	Derivative of Smoothed
2416	CIHEX.Volume_Log	Log of
2417	CIHEX.Volume_mva365	365 Day MA
2418	CIHEX.Volume_mva200	200 Day MA
2419	CIHEX.Volume_mva050	50 Day MA
2423	CIHEX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2425	CIHEX.Adjusted_SmoothDer	Derivative of Smoothed
2428	CIHEX.Adjusted_mva200	200 Day MA
2429	CIHEX.Adjusted_mva050	50 Day MA
2435	IMPCH_SmoothDer	Derivative of Smoothed U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
2447	EXPCH_mva365	U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA) 365 Day MA
2453	IMPMX_Smooth	Savitsky-Golay Smoothed (p=3, n=365) U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA)
2455	IMPMX_SmoothDer	Derivative of Smoothed U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA)
2465	EXPMX_SmoothDer	Derivative of Smoothed U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA)
2470	HSN1FNSA_YoY	New One Family Houses Sold: United States (Monthly, NSA) Year over Year
2472	HSN1FNSA_YoY5	New One Family Houses Sold: United States (Monthly, NSA) 5 Year over 5 Year
2478	HSN1FNSA_mva200	New One Family Houses Sold: United States (Monthly, NSA) 200 Day MA
2485	HNFSUSNSA_SmoothDer	Derivative of Smoothed New One Family Houses for Sale in the United States (Monthly, NSA)
2497	BUSLOANS_mva365	Commercial and Industrial Loans, All Commercial Banks (Monthly, SA) 365 Day MA
2507	TOTCI_mva365	Commercial and Industrial Loans, All Commercial Banks (Weekly, SA) 365 Day MA
2517	BUSLOANSNSA_mva365	Commercial and Industrial Loans, All Commercial Banks (Monthly, NSA) 365 Day MA
2526	REALLNNSA_Log	Log of Real Estate Loans, All Commercial Banks (Monthly, NSA)
2527	REALLNNSA_mva365	Real Estate Loans, All Commercial Banks (Monthly, NSA) 365 Day MA
2528	REALLNNSA_mva200	Real Estate Loans, All Commercial Banks (Monthly, NSA) 200 Day MA
2529	REALLNNSA_mva050	Real Estate Loans, All Commercial Banks (Monthly, NSA) 50 Day MA
2536	REALLN_Log	Log of Real Estate Loans, All Commercial Banks (Monthly, SA)
2537	REALLN_mva365	Real Estate Loans, All Commercial Banks (Monthly, SA) 365 Day MA
2538	REALLN_mva200	Real Estate Loans, All Commercial Banks (Monthly, SA) 200 Day MA
2539	REALLN_mva050	Real Estate Loans, All Commercial Banks (Monthly, SA) 50 Day MA
2547	RELACBW027NBOG_mva365	Real Estate Loans, All Commercial Banks (Weekly, NSA) 365 Day MA
2548	RELACBW027NBOG_mva200	Real Estate Loans, All Commercial Banks (Weekly, NSA) 200 Day MA
2549	RELACBW027NBOG_mva050	Real Estate Loans, All Commercial Banks (Weekly, NSA) 50 Day MA
2557	RELACBW027SBOG_mva365	Real Estate Loans, All Commercial Banks (Weekly, SA) 365 Day MA
2558	RELACBW027SBOG_mva200	Real Estate Loans, All Commercial Banks (Weekly, SA) 200 Day MA
2563	RREACBM027NBOG_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2566	RREACBM027NBOG_Log	Log of Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2567	RREACBM027NBOG_mva365	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA) 365 Day MA
2568	RREACBM027NBOG_mva200	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA) 200 Day MA
2569	RREACBM027NBOG_mva050	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA) 50 Day MA
2573	RREACBM027SBOG_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA)
2576	RREACBM027SBOG_Log	Log of Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA)
2577	RREACBM027SBOG_mva365	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA) 365 Day MA
2578	RREACBM027SBOG_mva200	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA) 200 Day MA
2579	RREACBM027SBOG_mva050	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA) 50 Day MA
2587	RREACBW027SBOG_mva365	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA) 365 Day MA
2588	RREACBW027SBOG_mva200	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA) 200 Day MA
2593	RREACBW027NBOG_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA)
2597	RREACBW027NBOG_mva365	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA) 365 Day MA
2598	RREACBW027NBOG_mva200	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA) 200 Day MA
2603	MORTGAGE30US_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 30-Year Fixed Rate Mortgage Average in the United States
2607	MORTGAGE30US_mva365	30-Year Fixed Rate Mortgage Average in the United States 365 Day MA
2609	MORTGAGE30US_mva050	30-Year Fixed Rate Mortgage Average in the United States 50 Day MA
2613	CONSUMERNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans, All Commercial Banks
2616	CONSUMERNSA_Log	Log of Consumer Loans, All Commercial Banks
2617	CONSUMERNSA_mva365	Consumer Loans, All Commercial Banks 365 Day MA
2618	CONSUMERNSA_mva200	Consumer Loans, All Commercial Banks 200 Day MA
2619	CONSUMERNSA_mva050	Consumer Loans, All Commercial Banks 50 Day MA
2627	TOTLLNSA_mva365	Loans and Leases in Bank Credit, All Commercial Banks 365 Day MA
2628	TOTLLNSA_mva200	Loans and Leases in Bank Credit, All Commercial Banks 200 Day MA
2646	DRCLACBS_Log	Log of Delinquency Rate on Consumer Loans, All Commercial Banks, SA
2647	DRCLACBS_mva365	Delinquency Rate on Consumer Loans, All Commercial Banks, SA 365 Day MA
2648	DRCLACBS_mva200	Delinquency Rate on Consumer Loans, All Commercial Banks, SA 200 Day MA
2649	DRCLACBS_mva050	Delinquency Rate on Consumer Loans, All Commercial Banks, SA 50 Day MA
2657	TOTCINSA_mva365	Commercial and Industrial Loans, All Commercial Banks (Weekly, NSA) 365 Day MA
2660	SRPSABSNNCB_YoY	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Year over Year
2661	SRPSABSNNCB_YoY4	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 4 Year over 4 Year
2662	SRPSABSNNCB_YoY5	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 5 Year over 5 Year
2666	SRPSABSNNCB_Log	Log of Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
2667	SRPSABSNNCB_mva365	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 365 Day MA
2668	SRPSABSNNCB_mva200	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 200 Day MA
2669	SRPSABSNNCB_mva050	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 50 Day MA
2676	ASTLL_Log	Log of All sectors; total loans; liability, Level (NSA)
2677	ASTLL_mva365	All sectors; total loans; liability, Level (NSA) 365 Day MA
2678	ASTLL_mva200	All sectors; total loans; liability, Level (NSA) 200 Day MA
2679	ASTLL_mva050	All sectors; total loans; liability, Level (NSA) 50 Day MA
2683	FBDILNECA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
2686	FBDILNECA_Log	Log of Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
2687	FBDILNECA_mva365	Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA) 365 Day MA
2688	FBDILNECA_mva200	Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA) 200 Day MA
2689	FBDILNECA_mva050	Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA) 50 Day MA
2691	ASOLAL_YoY4	All sectors; other loans and advances; liability, Level (NSA) 4 Year over 4 Year
2696	ASOLAL_Log	Log of All sectors; other loans and advances; liability, Level (NSA)
2697	ASOLAL_mva365	All sectors; other loans and advances; liability, Level (NSA) 365 Day MA
2698	ASOLAL_mva200	All sectors; other loans and advances; liability, Level (NSA) 200 Day MA
2699	ASOLAL_mva050	All sectors; other loans and advances; liability, Level (NSA) 50 Day MA
2706	ASTMA_Log	Log of All sectors; total mortgages; asset, Level (NSA)
2707	ASTMA_mva365	All sectors; total mortgages; asset, Level (NSA) 365 Day MA
2708	ASTMA_mva200	All sectors; total mortgages; asset, Level (NSA) 200 Day MA
2709	ASTMA_mva050	All sectors; total mortgages; asset, Level (NSA) 50 Day MA
2716	ASHMA_Log	Log of All sectors; home mortgages; asset, Level (NSA)
2717	ASHMA_mva365	All sectors; home mortgages; asset, Level (NSA) 365 Day MA
2718	ASHMA_mva200	All sectors; home mortgages; asset, Level (NSA) 200 Day MA
2719	ASHMA_mva050	All sectors; home mortgages; asset, Level (NSA) 50 Day MA
2723	ASMRMA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All sectors; multifamily residential mortgages; asset, Level (NSA)
2726	ASMRMA_Log	Log of All sectors; multifamily residential mortgages; asset, Level (NSA)
2727	ASMRMA_mva365	All sectors; multifamily residential mortgages; asset, Level (NSA) 365 Day MA
2728	ASMRMA_mva200	All sectors; multifamily residential mortgages; asset, Level (NSA) 200 Day MA
2729	ASMRMA_mva050	All sectors; multifamily residential mortgages; asset, Level (NSA) 50 Day MA
2736	ASCMA_Log	Log of All sectors; commercial mortgages; asset, Level (NSA)
2737	ASCMA_mva365	All sectors; commercial mortgages; asset, Level (NSA) 365 Day MA
2738	ASCMA_mva200	All sectors; commercial mortgages; asset, Level (NSA) 200 Day MA
2739	ASCMA_mva050	All sectors; commercial mortgages; asset, Level (NSA) 50 Day MA
2746	ASFMA_Log	Log of All sectors; farm mortgages; asset, Level (NSA)
2747	ASFMA_mva365	All sectors; farm mortgages; asset, Level (NSA) 365 Day MA
2748	ASFMA_mva200	All sectors; farm mortgages; asset, Level (NSA) 200 Day MA
2749	ASFMA_mva050	All sectors; farm mortgages; asset, Level (NSA) 50 Day MA
2753	CCLBSHNO_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Households and nonprofit organizations; consumer credit; liability, Level (NSA)
2757	CCLBSHNO_mva365	Households and nonprofit organizations; consumer credit; liability, Level (NSA) 365 Day MA
2766	FBDSILQ027S_Log	Log of Domestic financial sectors debt securities; liability, Level (NSA)
2767	FBDSILQ027S_mva365	Domestic financial sectors debt securities; liability, Level (NSA) 365 Day MA
2768	FBDSILQ027S_mva200	Domestic financial sectors debt securities; liability, Level (NSA) 200 Day MA
2769	FBDSILQ027S_mva050	Domestic financial sectors debt securities; liability, Level (NSA) 50 Day MA
2772	FBLL_YoY5	Domestic financial sectors loans; liability, Level (NSA) 5 Year over 5 Year
2776	FBLL_Log	Log of Domestic financial sectors loans; liability, Level (NSA)
2777	FBLL_mva365	Domestic financial sectors loans; liability, Level (NSA) 365 Day MA
2778	FBLL_mva200	Domestic financial sectors loans; liability, Level (NSA) 200 Day MA
2779	FBLL_mva050	Domestic financial sectors loans; liability, Level (NSA) 50 Day MA
2786	NCBDBIQ027S_Log	Log of Nonfinancial corporate business; debt securities; liability, Level
2787	NCBDBIQ027S_mva365	Nonfinancial corporate business; debt securities; liability, Level 365 Day MA
2788	NCBDBIQ027S_mva200	Nonfinancial corporate business; debt securities; liability, Level 200 Day MA
2789	NCBDBIQ027S_mva050	Nonfinancial corporate business; debt securities; liability, Level 50 Day MA
2797	DGS10_mva365	10-Year Treasury Constant Maturity Rate 365 Day MA
2807	TNX.Open_mva365	365 Day MA
2817	TNX.High_mva365	365 Day MA
2827	TNX.Low_mva365	365 Day MA
2837	TNX.Close_mva365	365 Day MA
2840	TNX.Volume_YoY	Year over Year
2841	TNX.Volume_YoY4	4 Year over 4 Year
2842	TNX.Volume_YoY5	5 Year over 5 Year
2843	TNX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2844	TNX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2845	TNX.Volume_SmoothDer	Derivative of Smoothed
2846	TNX.Volume_Log	Log of
2847	TNX.Volume_mva365	365 Day MA
2848	TNX.Volume_mva200	200 Day MA
2849	TNX.Volume_mva050	50 Day MA
2857	TNX.Adjusted_mva365	365 Day MA
2865	CLF.Open_SmoothDer	Derivative of Smoothed
2866	CLF.Open_Log	Log of
2875	CLF.High_SmoothDer	Derivative of Smoothed
2885	CLF.Low_SmoothDer	Derivative of Smoothed
2886	CLF.Low_Log	Log of
2895	CLF.Close_SmoothDer	Derivative of Smoothed
2896	CLF.Close_Log	Log of
2904	CLF.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2906	CLF.Volume_Log	Log of
2915	CLF.Adjusted_SmoothDer	Derivative of Smoothed
2916	CLF.Adjusted_Log	Log of
2923	DGS30_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 10-Year Treasury Constant Maturity Rate
2927	DGS30_mva365	10-Year Treasury Constant Maturity Rate 365 Day MA
2929	DGS30_mva050	10-Year Treasury Constant Maturity Rate 50 Day MA
2933	DGS1_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 1-Year Treasury Constant Maturity Rate
2937	DGS1_mva365	1-Year Treasury Constant Maturity Rate 365 Day MA
2938	DGS1_mva200	1-Year Treasury Constant Maturity Rate 200 Day MA
2939	DGS1_mva050	1-Year Treasury Constant Maturity Rate 50 Day MA
2943	DGS2_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 2-Year Treasury Constant Maturity Rate
2947	DGS2_mva365	2-Year Treasury Constant Maturity Rate 365 Day MA
2951	TB3MS_YoY4	3-Month Treasury Bill: Secondary Market Rate (Monthly) 4 Year over 4 Year
2953	TB3MS_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2956	TB3MS_Log	Log of 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2957	TB3MS_mva365	3-Month Treasury Bill: Secondary Market Rate (Monthly) 365 Day MA
2958	TB3MS_mva200	3-Month Treasury Bill: Secondary Market Rate (Monthly) 200 Day MA
2959	TB3MS_mva050	3-Month Treasury Bill: Secondary Market Rate (Monthly) 50 Day MA
2963	DTB3_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 3-Month Treasury Bill: Secondary Market Rate (Daily)
2966	DTB3_Log	Log of 3-Month Treasury Bill: Secondary Market Rate (Daily)
2967	DTB3_mva365	3-Month Treasury Bill: Secondary Market Rate (Daily) 365 Day MA
2968	DTB3_mva200	3-Month Treasury Bill: Secondary Market Rate (Daily) 200 Day MA
2969	DTB3_mva050	3-Month Treasury Bill: Secondary Market Rate (Daily) 50 Day MA
2971	IRX.Open_YoY4	4 Year over 4 Year
2973	IRX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2976	IRX.Open_Log	Log of
2977	IRX.Open_mva365	365 Day MA
2978	IRX.Open_mva200	200 Day MA
2979	IRX.Open_mva050	50 Day MA
2981	IRX.High_YoY4	4 Year over 4 Year
2983	IRX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2986	IRX.High_Log	Log of
2987	IRX.High_mva365	365 Day MA
2988	IRX.High_mva200	200 Day MA
2989	IRX.High_mva050	50 Day MA
2993	IRX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2996	IRX.Low_Log	Log of
2997	IRX.Low_mva365	365 Day MA
2998	IRX.Low_mva200	200 Day MA
3001	IRX.Close_YoY4	4 Year over 4 Year
3003	IRX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3006	IRX.Close_Log	Log of
3007	IRX.Close_mva365	365 Day MA
3008	IRX.Close_mva200	200 Day MA
3009	IRX.Close_mva050	50 Day MA
3010	IRX.Volume_YoY	Year over Year
3011	IRX.Volume_YoY4	4 Year over 4 Year
3012	IRX.Volume_YoY5	5 Year over 5 Year
3013	IRX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3014	IRX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
3015	IRX.Volume_SmoothDer	Derivative of Smoothed
3016	IRX.Volume_Log	Log of
3017	IRX.Volume_mva365	365 Day MA
3018	IRX.Volume_mva200	200 Day MA
3019	IRX.Volume_mva050	50 Day MA
3021	IRX.Adjusted_YoY4	4 Year over 4 Year
3023	IRX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3026	IRX.Adjusted_Log	Log of
3027	IRX.Adjusted_mva365	365 Day MA
3028	IRX.Adjusted_mva200	200 Day MA
3029	IRX.Adjusted_mva050	50 Day MA
3035	DCOILWTICO_SmoothDer	Derivative of Smoothed Crude Oil Prices: West Texas Intermediate (WTI) Cushing, Oklahoma
3036	DCOILWTICO_Log	Log of Crude Oil Prices: West Texas Intermediate (WTI) Cushing, Oklahoma
3045	DCOILBRENTEU_SmoothDer	Derivative of Smoothed Crude Oil Prices: Brent - Europe
3051	NEWORDER_YoY4	Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft 4 Year over 4 Year
3053	NEWORDER_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft
3055	NEWORDER_SmoothDer	Derivative of Smoothed Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft
3056	NEWORDER_Log	Log of Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft
3057	NEWORDER_mva365	Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft 365 Day MA
3058	NEWORDER_mva200	Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft 200 Day MA
3059	NEWORDER_mva050	Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft 50 Day MA
3067	ALTSALES_mva365	Light Weight Vehicle Sales: Autos and Light Trucks 365 Day MA
3068	ALTSALES_mva200	Light Weight Vehicle Sales: Autos and Light Trucks 200 Day MA
3070	ICSA_YoY	Initial Jobless Claims Year over Year
3073	ICSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Initial Jobless Claims
3075	ICSA_SmoothDer	Derivative of Smoothed Initial Jobless Claims
3076	ICSA_Log	Log of Initial Jobless Claims
3077	ICSA_mva365	Initial Jobless Claims 365 Day MA
3078	ICSA_mva200	Initial Jobless Claims 200 Day MA
3079	ICSA_mva050	Initial Jobless Claims 50 Day MA
3083	GSPC.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3085	GSPC.Open_SmoothDer	Derivative of Smoothed
3088	GSPC.Open_mva200	200 Day MA
3089	GSPC.Open_mva050	50 Day MA
3093	GSPC.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3095	GSPC.High_SmoothDer	Derivative of Smoothed
3098	GSPC.High_mva200	200 Day MA
3099	GSPC.High_mva050	50 Day MA
3103	GSPC.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3105	GSPC.Low_SmoothDer	Derivative of Smoothed
3108	GSPC.Low_mva200	200 Day MA
3109	GSPC.Low_mva050	50 Day MA
3113	GSPC.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3115	GSPC.Close_SmoothDer	Derivative of Smoothed
3118	GSPC.Close_mva200	200 Day MA
3119	GSPC.Close_mva050	50 Day MA
3125	GSPC.Volume_SmoothDer	Derivative of Smoothed
3133	GSPC.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3135	GSPC.Adjusted_SmoothDer	Derivative of Smoothed
3138	GSPC.Adjusted_mva200	200 Day MA
3139	GSPC.Adjusted_mva050	50 Day MA
3143	FXAIX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3145	FXAIX.Open_SmoothDer	Derivative of Smoothed
3148	FXAIX.Open_mva200	200 Day MA
3149	FXAIX.Open_mva050	50 Day MA
3153	FXAIX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3155	FXAIX.High_SmoothDer	Derivative of Smoothed
3158	FXAIX.High_mva200	200 Day MA
3159	FXAIX.High_mva050	50 Day MA
3163	FXAIX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3165	FXAIX.Low_SmoothDer	Derivative of Smoothed
3168	FXAIX.Low_mva200	200 Day MA
3169	FXAIX.Low_mva050	50 Day MA
3173	FXAIX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3175	FXAIX.Close_SmoothDer	Derivative of Smoothed
3178	FXAIX.Close_mva200	200 Day MA
3179	FXAIX.Close_mva050	50 Day MA
3180	FXAIX.Volume_YoY	Year over Year
3181	FXAIX.Volume_YoY4	4 Year over 4 Year
3182	FXAIX.Volume_YoY5	5 Year over 5 Year
3183	FXAIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3184	FXAIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
3185	FXAIX.Volume_SmoothDer	Derivative of Smoothed
3186	FXAIX.Volume_Log	Log of
3187	FXAIX.Volume_mva365	365 Day MA
3188	FXAIX.Volume_mva200	200 Day MA
3189	FXAIX.Volume_mva050	50 Day MA
3193	FXAIX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3195	FXAIX.Adjusted_SmoothDer	Derivative of Smoothed
3198	FXAIX.Adjusted_mva200	200 Day MA
3199	FXAIX.Adjusted_mva050	50 Day MA
3208	FTIHX.Open_mva200	200 Day MA
3209	FTIHX.Open_mva050	50 Day MA
3218	FTIHX.High_mva200	200 Day MA
3219	FTIHX.High_mva050	50 Day MA
3228	FTIHX.Low_mva200	200 Day MA
3229	FTIHX.Low_mva050	50 Day MA
3238	FTIHX.Close_mva200	200 Day MA
3239	FTIHX.Close_mva050	50 Day MA
3240	FTIHX.Volume_YoY	Year over Year
3241	FTIHX.Volume_YoY4	4 Year over 4 Year
3242	FTIHX.Volume_YoY5	5 Year over 5 Year
3243	FTIHX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3244	FTIHX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
3245	FTIHX.Volume_SmoothDer	Derivative of Smoothed
3246	FTIHX.Volume_Log	Log of
3247	FTIHX.Volume_mva365	365 Day MA
3248	FTIHX.Volume_mva200	200 Day MA
3249	FTIHX.Volume_mva050	50 Day MA
3258	FTIHX.Adjusted_mva200	200 Day MA
3259	FTIHX.Adjusted_mva050	50 Day MA
3268	MDIZX.Open_mva200	200 Day MA
3269	MDIZX.Open_mva050	50 Day MA
3278	MDIZX.High_mva200	200 Day MA
3279	MDIZX.High_mva050	50 Day MA
3288	MDIZX.Low_mva200	200 Day MA
3289	MDIZX.Low_mva050	50 Day MA
3298	MDIZX.Close_mva200	200 Day MA
3299	MDIZX.Close_mva050	50 Day MA
3300	MDIZX.Volume_YoY	Year over Year
3301	MDIZX.Volume_YoY4	4 Year over 4 Year
3302	MDIZX.Volume_YoY5	5 Year over 5 Year
3303	MDIZX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3304	MDIZX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
3305	MDIZX.Volume_SmoothDer	Derivative of Smoothed
3306	MDIZX.Volume_Log	Log of
3307	MDIZX.Volume_mva365	365 Day MA
3308	MDIZX.Volume_mva200	200 Day MA
3309	MDIZX.Volume_mva050	50 Day MA
3318	MDIZX.Adjusted_mva200	200 Day MA
3319	MDIZX.Adjusted_mva050	50 Day MA
3328	DODIX.Open_mva200	200 Day MA
3338	DODIX.High_mva200	200 Day MA
3348	DODIX.Low_mva200	200 Day MA
3358	DODIX.Close_mva200	200 Day MA
3360	DODIX.Volume_YoY	Year over Year
3361	DODIX.Volume_YoY4	4 Year over 4 Year
3362	DODIX.Volume_YoY5	5 Year over 5 Year
3363	DODIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3364	DODIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
3365	DODIX.Volume_SmoothDer	Derivative of Smoothed
3366	DODIX.Volume_Log	Log of
3367	DODIX.Volume_mva365	365 Day MA
3368	DODIX.Volume_mva200	200 Day MA
3369	DODIX.Volume_mva050	50 Day MA
3378	DODIX.Adjusted_mva200	200 Day MA
3383	RLG.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3385	RLG.Open_SmoothDer	Derivative of Smoothed
3388	RLG.Open_mva200	200 Day MA
3389	RLG.Open_mva050	50 Day MA
3393	RLG.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3395	RLG.High_SmoothDer	Derivative of Smoothed
3398	RLG.High_mva200	200 Day MA
3399	RLG.High_mva050	50 Day MA
3403	RLG.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3405	RLG.Low_SmoothDer	Derivative of Smoothed
3408	RLG.Low_mva200	200 Day MA
3409	RLG.Low_mva050	50 Day MA
3413	RLG.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3415	RLG.Close_SmoothDer	Derivative of Smoothed
3418	RLG.Close_mva200	200 Day MA
3419	RLG.Close_mva050	50 Day MA
3420	RLG.Volume_YoY	Year over Year
3421	RLG.Volume_YoY4	4 Year over 4 Year
3422	RLG.Volume_YoY5	5 Year over 5 Year
3423	RLG.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3424	RLG.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
3425	RLG.Volume_SmoothDer	Derivative of Smoothed
3426	RLG.Volume_Log	Log of
3427	RLG.Volume_mva365	365 Day MA
3428	RLG.Volume_mva200	200 Day MA
3429	RLG.Volume_mva050	50 Day MA
3433	RLG.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3435	RLG.Adjusted_SmoothDer	Derivative of Smoothed
3438	RLG.Adjusted_mva200	200 Day MA
3439	RLG.Adjusted_mva050	50 Day MA
3483	DJI.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3485	DJI.Volume_SmoothDer	Derivative of Smoothed
3507	STOXX50E.Open_mva365	365 Day MA
3508	STOXX50E.Open_mva200	200 Day MA
3517	STOXX50E.High_mva365	365 Day MA
3518	STOXX50E.High_mva200	200 Day MA
3527	STOXX50E.Low_mva365	365 Day MA
3528	STOXX50E.Low_mva200	200 Day MA
3537	STOXX50E.Close_mva365	365 Day MA
3538	STOXX50E.Close_mva200	200 Day MA
3546	STOXX50E.Volume_Log	Log of
3557	STOXX50E.Adjusted_mva365	365 Day MA
3558	STOXX50E.Adjusted_mva200	200 Day MA
3568	EFA.Open_mva200	200 Day MA
3578	EFA.High_mva200	200 Day MA
3588	EFA.Low_mva200	200 Day MA
3598	EFA.Close_mva200	200 Day MA
3617	EFA.Adjusted_mva365	365 Day MA
3618	EFA.Adjusted_mva200	200 Day MA
3626	GDP_Log	Log of Gross Domestic Product
3627	GDP_mva365	Gross Domestic Product 365 Day MA
3628	GDP_mva200	Gross Domestic Product 200 Day MA
3629	GDP_mva050	Gross Domestic Product 50 Day MA
3636	FNDEFX_Log	Log of Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate)
3637	FNDEFX_mva365	Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate) 365 Day MA
3638	FNDEFX_mva200	Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate) 200 Day MA
3639	FNDEFX_mva050	Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate) 50 Day MA
3646	FDEFX_Log	Log of Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
3647	FDEFX_mva365	Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 365 Day MA
3648	FDEFX_mva200	Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 200 Day MA
3649	FDEFX_mva050	Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 50 Day MA
3653	GDPNOW_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Fed Atlanta GDPNow
3655	GDPNOW_SmoothDer	Derivative of Smoothed Fed Atlanta GDPNow
3656	GDPNOW_Log	Log of Fed Atlanta GDPNow
3666	GDPC1_Log	Log of Real Gross Domestic Product
3667	GDPC1_mva365	Real Gross Domestic Product 365 Day MA
3668	GDPC1_mva200	Real Gross Domestic Product 200 Day MA
3669	GDPC1_mva050	Real Gross Domestic Product 50 Day MA
3676	GDPDEF_Log	Log of Gross Domestic Product: Implicit Price Deflator
3677	GDPDEF_mva365	Gross Domestic Product: Implicit Price Deflator 365 Day MA
3678	GDPDEF_mva200	Gross Domestic Product: Implicit Price Deflator 200 Day MA
3679	GDPDEF_mva050	Gross Domestic Product: Implicit Price Deflator 50 Day MA
3683	VIG.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3688	VIG.Open_mva200	200 Day MA
3689	VIG.Open_mva050	50 Day MA
3693	VIG.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3698	VIG.High_mva200	200 Day MA
3699	VIG.High_mva050	50 Day MA
3703	VIG.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3708	VIG.Low_mva200	200 Day MA
3709	VIG.Low_mva050	50 Day MA
3713	VIG.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3718	VIG.Close_mva200	200 Day MA
3719	VIG.Close_mva050	50 Day MA
3733	VIG.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3738	VIG.Adjusted_mva200	200 Day MA
3739	VIG.Adjusted_mva050	50 Day MA
3743	WLRRAL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3745	WLRRAL_SmoothDer	Derivative of Smoothed Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3756	FEDFUNDS_Log	Log of Effective Federal Funds Rate
3757	FEDFUNDS_mva365	Effective Federal Funds Rate 365 Day MA
3758	FEDFUNDS_mva200	Effective Federal Funds Rate 200 Day MA
3759	FEDFUNDS_mva050	Effective Federal Funds Rate 50 Day MA
3760	GPDI_YoY	Gross Private Domestic Investment Year over Year
3770	W790RC1Q027SBEA_YoY	Net domestic investment: Private: Domestic busines Year over Year
3775	W790RC1Q027SBEA_SmoothDer	Derivative of Smoothed Net domestic investment: Private: Domestic busines
3776	W790RC1Q027SBEA_Log	Log of Net domestic investment: Private: Domestic busines
3780	MZMV_YoY	Velocity of MZM Money Stock Year over Year
3781	MZMV_YoY4	Velocity of MZM Money Stock 4 Year over 4 Year
3786	MZMV_Log	Log of Velocity of MZM Money Stock
3787	MZMV_mva365	Velocity of MZM Money Stock 365 Day MA
3788	MZMV_mva200	Velocity of MZM Money Stock 200 Day MA
3789	MZMV_mva050	Velocity of MZM Money Stock 50 Day MA
3790	M1_YoY	M1 Money Stock Year over Year
3796	M1_Log	Log of M1 Money Stock
3797	M1_mva365	M1 Money Stock 365 Day MA
3798	M1_mva200	M1 Money Stock 200 Day MA
3799	M1_mva050	M1 Money Stock 50 Day MA
3800	M2_YoY	M2 Money Stock Year over Year
3806	M2_Log	Log of M2 Money Stock
3807	M2_mva365	M2 Money Stock 365 Day MA
3808	M2_mva200	M2 Money Stock 200 Day MA
3809	M2_mva050	M2 Money Stock 50 Day MA
3821	IPMAN_YoY4	Industrial Production: Manufacturing (NAICS) 4 Year over 4 Year
3823	IPMAN_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Industrial Production: Manufacturing (NAICS)
3825	IPMAN_SmoothDer	Derivative of Smoothed Industrial Production: Manufacturing (NAICS)
3826	IPMAN_Log	Log of Industrial Production: Manufacturing (NAICS)
3829	IPMAN_mva050	Industrial Production: Manufacturing (NAICS) 50 Day MA
3891	GS5_YoY4	5-Year Treasury Constant Maturity Rate 4 Year over 4 Year
3897	GS5_mva365	5-Year Treasury Constant Maturity Rate 365 Day MA
3907	PSAVERT_mva365	Personal Saving Rate 365 Day MA
3908	PSAVERT_mva200	Personal Saving Rate 200 Day MA
3925	VXX.Open_SmoothDer	Derivative of Smoothed
3935	VXX.High_SmoothDer	Derivative of Smoothed
3945	VXX.Low_SmoothDer	Derivative of Smoothed
3955	VXX.Close_SmoothDer	Derivative of Smoothed
3963	VXX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3966	VXX.Volume_Log	Log of
3968	VXX.Volume_mva200	200 Day MA
3975	VXX.Adjusted_SmoothDer	Derivative of Smoothed
3980	HOUST1F_YoY	Privately Owned Housing Starts: 1-Unit Structures Year over Year
3983	HOUST1F_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Privately Owned Housing Starts: 1-Unit Structures
3985	HOUST1F_SmoothDer	Derivative of Smoothed Privately Owned Housing Starts: 1-Unit Structures
3986	HOUST1F_Log	Log of Privately Owned Housing Starts: 1-Unit Structures
3988	HOUST1F_mva200	Privately Owned Housing Starts: 1-Unit Structures 200 Day MA
3989	HOUST1F_mva050	Privately Owned Housing Starts: 1-Unit Structures 50 Day MA
3993	GFDEBTN_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Federal Debt: Total Public Debt
3996	GFDEBTN_Log	Log of Federal Debt: Total Public Debt
3997	GFDEBTN_mva365	Federal Debt: Total Public Debt 365 Day MA
3998	GFDEBTN_mva200	Federal Debt: Total Public Debt 200 Day MA
3999	GFDEBTN_mva050	Federal Debt: Total Public Debt 50 Day MA
4000	HOUST_YoY	Housing Starts: Total: New Privately Owned Housing Units Started, SA Year over Year
4003	HOUST_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Housing Starts: Total: New Privately Owned Housing Units Started, SA
4005	HOUST_SmoothDer	Derivative of Smoothed Housing Starts: Total: New Privately Owned Housing Units Started, SA
4006	HOUST_Log	Log of Housing Starts: Total: New Privately Owned Housing Units Started, SA
4008	HOUST_mva200	Housing Starts: Total: New Privately Owned Housing Units Started, SA 200 Day MA
4009	HOUST_mva050	Housing Starts: Total: New Privately Owned Housing Units Started, SA 50 Day MA
4010	HOUSTNSA_YoY	Housing Starts: Total: New Privately Owned Housing Units Started, NSA Year over Year
4011	HOUSTNSA_YoY4	Housing Starts: Total: New Privately Owned Housing Units Started, NSA 4 Year over 4 Year
4012	HOUSTNSA_YoY5	Housing Starts: Total: New Privately Owned Housing Units Started, NSA 5 Year over 5 Year
4013	HOUSTNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Housing Starts: Total: New Privately Owned Housing Units Started, NSA
4015	HOUSTNSA_SmoothDer	Derivative of Smoothed Housing Starts: Total: New Privately Owned Housing Units Started, NSA
4016	HOUSTNSA_Log	Log of Housing Starts: Total: New Privately Owned Housing Units Started, NSA
4018	HOUSTNSA_mva200	Housing Starts: Total: New Privately Owned Housing Units Started, NSA 200 Day MA
4019	HOUSTNSA_mva050	Housing Starts: Total: New Privately Owned Housing Units Started, NSA 50 Day MA
4020	EXHOSLUSM495S_YoY	Existing Home Sales Year over Year
4025	EXHOSLUSM495S_SmoothDer	Derivative of Smoothed Existing Home Sales
4035	MSPUS_SmoothDer	Derivative of Smoothed Median Sales Price of Houses Sold for the United States (NSA)
4041	UMDMNO_YoY4	Manufacturers’ New Orders: Durable Goods (NSA) 4 Year over 4 Year
4053	DGORDER_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Manufacturers’ New Orders: Durable Goods (SA)
4055	DGORDER_SmoothDer	Derivative of Smoothed Manufacturers’ New Orders: Durable Goods (SA)
4056	DGORDER_Log	Log of Manufacturers’ New Orders: Durable Goods (SA)
4057	DGORDER_mva365	Manufacturers’ New Orders: Durable Goods (SA) 365 Day MA
4058	DGORDER_mva200	Manufacturers’ New Orders: Durable Goods (SA) 200 Day MA
4059	DGORDER_mva050	Manufacturers’ New Orders: Durable Goods (SA) 50 Day MA
4063	CSUSHPINSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) S&P/Case-Shiller U.S. National Home Price Index (NSA)
4065	CSUSHPINSA_SmoothDer	Derivative of Smoothed S&P/Case-Shiller U.S. National Home Price Index (NSA)
4066	CSUSHPINSA_Log	Log of S&P/Case-Shiller U.S. National Home Price Index (NSA)
4069	CSUSHPINSA_mva050	S&P/Case-Shiller U.S. National Home Price Index (NSA) 50 Day MA
4070	GFDEGDQ188S_YoY	Federal Debt: Total Public Debt as Percent of Gross Domestic Product Year over Year
4075	GFDEGDQ188S_SmoothDer	Derivative of Smoothed Federal Debt: Total Public Debt as Percent of Gross Domestic Product
4080	FYFSD_YoY	Federal Surplus or Deficit Year over Year
4083	FYFSD_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Federal Surplus or Deficit
4084	FYFSD_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) Federal Surplus or Deficit
4086	FYFSD_Log	Log of Federal Surplus or Deficit
4087	FYFSD_mva365	Federal Surplus or Deficit 365 Day MA
4088	FYFSD_mva200	Federal Surplus or Deficit 200 Day MA
4089	FYFSD_mva050	Federal Surplus or Deficit 50 Day MA
4090	FYFSGDA188S_YoY	Federal Surplus or Deficit [-] as Percent of Gross Domestic Product Year over Year
4094	FYFSGDA188S_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
4096	FYFSGDA188S_Log	Log of Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
4097	FYFSGDA188S_mva365	Federal Surplus or Deficit [-] as Percent of Gross Domestic Product 365 Day MA
4098	FYFSGDA188S_mva200	Federal Surplus or Deficit [-] as Percent of Gross Domestic Product 200 Day MA
4099	FYFSGDA188S_mva050	Federal Surplus or Deficit [-] as Percent of Gross Domestic Product 50 Day MA
4108	GDX.Open_mva200	200 Day MA
4118	GDX.High_mva200	200 Day MA
4128	GDX.Low_mva200	200 Day MA
4138	GDX.Close_mva200	200 Day MA
4158	GDX.Adjusted_mva200	200 Day MA
4205	XLE.Volume_SmoothDer	Derivative of Smoothed
4225	GSG.Open_SmoothDer	Derivative of Smoothed
4235	GSG.High_SmoothDer	Derivative of Smoothed
4245	GSG.Low_SmoothDer	Derivative of Smoothed
4255	GSG.Close_SmoothDer	Derivative of Smoothed
4275	GSG.Adjusted_SmoothDer	Derivative of Smoothed
4285	WALCL_SmoothDer	Derivative of Smoothed All Federal Reserve Banks: Total Assets
4291	OUTMS_YoY4	Manufacturing Sector: Real Output 4 Year over 4 Year
4295	OUTMS_SmoothDer	Derivative of Smoothed Manufacturing Sector: Real Output
4307	MANEMP_mva365	All Employees: Manufacturing 365 Day MA
4308	MANEMP_mva200	All Employees: Manufacturing 200 Day MA
4310	PRS30006163_YoY	Manufacturing Sector: Real Output Per Person Year over Year
4311	PRS30006163_YoY4	Manufacturing Sector: Real Output Per Person 4 Year over 4 Year
4315	PRS30006163_SmoothDer	Derivative of Smoothed Manufacturing Sector: Real Output Per Person
4323	BAMLC0A3CA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) ICE BofAML US Corporate A Option-Adjusted Spread
4325	BAMLC0A3CA_SmoothDer	Derivative of Smoothed ICE BofAML US Corporate A Option-Adjusted Spread
4331	AAA_YoY4	Moody’s Seasoned Aaa Corporate Bond Yield 4 Year over 4 Year
4333	AAA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Moody’s Seasoned Aaa Corporate Bond Yield
4335	AAA_SmoothDer	Derivative of Smoothed Moody’s Seasoned Aaa Corporate Bond Yield
4336	AAA_Log	Log of Moody’s Seasoned Aaa Corporate Bond Yield
4337	AAA_mva365	Moody’s Seasoned Aaa Corporate Bond Yield 365 Day MA
4339	AAA_mva050	Moody’s Seasoned Aaa Corporate Bond Yield 50 Day MA
4347	SOFR_mva365	Secured Overnight Financing Rate 365 Day MA
4348	SOFR_mva200	Secured Overnight Financing Rate 200 Day MA
4349	SOFR_mva050	Secured Overnight Financing Rate 50 Day MA
4353	SOFRVOL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Volume
4355	SOFRVOL_SmoothDer	Derivative of Smoothed Secured Overnight Financing Volume
4357	SOFRVOL_mva365	Secured Overnight Financing Volume 365 Day MA
4358	SOFRVOL_mva200	Secured Overnight Financing Volume 200 Day MA
4367	SOFR99_mva365	Secured Overnight Financing Rate: 99th Percentile 365 Day MA
4368	SOFR99_mva200	Secured Overnight Financing Rate: 99th Percentile 200 Day MA
4369	SOFR99_mva050	Secured Overnight Financing Rate: 99th Percentile 50 Day MA
4377	SOFR75_mva365	Secured Overnight Financing Rate: 75th Percentile 365 Day MA
4378	SOFR75_mva200	Secured Overnight Financing Rate: 75th Percentile 200 Day MA
4379	SOFR75_mva050	Secured Overnight Financing Rate: 75th Percentile 50 Day MA
4386	SOFR25_Log	Log of Secured Overnight Financing Rate: 25th Percentile
4387	SOFR25_mva365	Secured Overnight Financing Rate: 25th Percentile 365 Day MA
4388	SOFR25_mva200	Secured Overnight Financing Rate: 25th Percentile 200 Day MA
4389	SOFR25_mva050	Secured Overnight Financing Rate: 25th Percentile 50 Day MA
4396	SOFR1_Log	Log of Secured Overnight Financing Rate: 1st Percentile
4397	SOFR1_mva365	Secured Overnight Financing Rate: 1st Percentile 365 Day MA
4398	SOFR1_mva200	Secured Overnight Financing Rate: 1st Percentile 200 Day MA
4399	SOFR1_mva050	Secured Overnight Financing Rate: 1st Percentile 50 Day MA
4407	OBFR_mva365	Overnight Bank Funding Rate 365 Day MA
4408	OBFR_mva200	Overnight Bank Funding Rate 200 Day MA
4409	OBFR_mva050	Overnight Bank Funding Rate 50 Day MA
4417	OBFR99_mva365	Overnight Bank Funding Rate: 99th Percentile 365 Day MA
4418	OBFR99_mva200	Overnight Bank Funding Rate: 99th Percentile 200 Day MA
4419	OBFR99_mva050	Overnight Bank Funding Rate: 99th Percentile 50 Day MA
4426	OBFR75_Log	Log of Overnight Bank Funding Rate: 75th Percentile
4427	OBFR75_mva365	Overnight Bank Funding Rate: 75th Percentile 365 Day MA
4428	OBFR75_mva200	Overnight Bank Funding Rate: 75th Percentile 200 Day MA
4429	OBFR75_mva050	Overnight Bank Funding Rate: 75th Percentile 50 Day MA
4437	OBFR25_mva365	Overnight Bank Funding Rate: 25th Percentile 365 Day MA
4438	OBFR25_mva200	Overnight Bank Funding Rate: 25th Percentile 200 Day MA
4439	OBFR25_mva050	Overnight Bank Funding Rate: 25th Percentile 50 Day MA
4446	OBFR1_Log	Log of Overnight Bank Funding Rate: 1st Percentile
4447	OBFR1_mva365	Overnight Bank Funding Rate: 1st Percentile 365 Day MA
4448	OBFR1_mva200	Overnight Bank Funding Rate: 1st Percentile 200 Day MA
4449	OBFR1_mva050	Overnight Bank Funding Rate: 1st Percentile 50 Day MA
4450	RPONTSYD_YoY	Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations Year over Year
4453	RPONTSYD_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations
4455	RPONTSYD_SmoothDer	Derivative of Smoothed Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations
4456	RPONTSYD_Log	Log of Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations
4457	RPONTSYD_mva365	Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations 365 Day MA
4460	IOER_YoY	Interest Rate on Excess Reserves Year over Year
4461	IOER_YoY4	Interest Rate on Excess Reserves 4 Year over 4 Year
4466	IOER_Log	Log of Interest Rate on Excess Reserves
4467	IOER_mva365	Interest Rate on Excess Reserves 365 Day MA
4468	IOER_mva200	Interest Rate on Excess Reserves 200 Day MA
4469	IOER_mva050	Interest Rate on Excess Reserves 50 Day MA
4470	WRESBAL_YoY	Reserve Balances with Federal Reserve Banks Year over Year
4472	WRESBAL_YoY5	Reserve Balances with Federal Reserve Banks 5 Year over 5 Year
4473	WRESBAL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Reserve Balances with Federal Reserve Banks
4475	WRESBAL_SmoothDer	Derivative of Smoothed Reserve Balances with Federal Reserve Banks
4480	EXCSRESNW_YoY	Excess Reserves of Depository Institutions Year over Year
4482	EXCSRESNW_YoY5	Excess Reserves of Depository Institutions 5 Year over 5 Year
4486	EXCSRESNW_Log	Log of Excess Reserves of Depository Institutions
4487	EXCSRESNW_mva365	Excess Reserves of Depository Institutions 365 Day MA
4488	EXCSRESNW_mva200	Excess Reserves of Depository Institutions 200 Day MA
4489	EXCSRESNW_mva050	Excess Reserves of Depository Institutions 50 Day MA
4490	ECBASSETS_YoY	Central Bank Assets for Euro Area (11-19 Countries) Year over Year
4491	ECBASSETS_YoY4	Central Bank Assets for Euro Area (11-19 Countries) 4 Year over 4 Year
4496	ECBASSETS_Log	Log of Central Bank Assets for Euro Area (11-19 Countries)
4497	ECBASSETS_mva365	Central Bank Assets for Euro Area (11-19 Countries) 365 Day MA
4498	ECBASSETS_mva200	Central Bank Assets for Euro Area (11-19 Countries) 200 Day MA
4499	ECBASSETS_mva050	Central Bank Assets for Euro Area (11-19 Countries) 50 Day MA
4506	EUNNGDP_Log	Log of Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
4507	EUNNGDP_mva365	Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries) 365 Day MA
4508	EUNNGDP_mva200	Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries) 200 Day MA
4509	EUNNGDP_mva050	Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries) 50 Day MA
4513	CEU0600000007_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
4515	CEU0600000007_SmoothDer	Derivative of Smoothed Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
4516	CEU0600000007_Log	Log of Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
4519	CEU0600000007_mva050	Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing 50 Day MA
4520	CURRENCY_YoY	Currency Component of M1 (Seasonally Adjusted) Year over Year
4526	CURRENCY_Log	Log of Currency Component of M1 (Seasonally Adjusted)
4527	CURRENCY_mva365	Currency Component of M1 (Seasonally Adjusted) 365 Day MA
4528	CURRENCY_mva200	Currency Component of M1 (Seasonally Adjusted) 200 Day MA
4529	CURRENCY_mva050	Currency Component of M1 (Seasonally Adjusted) 50 Day MA
4533	WCURRNS_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Currency Component of M1
4535	WCURRNS_SmoothDer	Derivative of Smoothed Currency Component of M1
4536	WCURRNS_Log	Log of Currency Component of M1
4537	WCURRNS_mva365	Currency Component of M1 365 Day MA
4538	WCURRNS_mva200	Currency Component of M1 200 Day MA
4539	WCURRNS_mva050	Currency Component of M1 50 Day MA
4542	BOGMBASE_YoY5	Monetary Base; Total 5 Year over 5 Year
4543	BOGMBASE_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Monetary Base; Total
4546	BOGMBASE_Log	Log of Monetary Base; Total
4548	BOGMBASE_mva200	Monetary Base; Total 200 Day MA
4549	BOGMBASE_mva050	Monetary Base; Total 50 Day MA
4555	PRS88003193_SmoothDer	Derivative of Smoothed Nonfinancial Corporations Sector: Unit Profits
4565	PPIACO_SmoothDer	Derivative of Smoothed Producer Price Index for All Commodities
4575	PCUOMFGOMFG_SmoothDer	Derivative of Smoothed Producer Price Index by Industry: Total Manufacturing Industries
4592	POPTHM_Log	Log of Population (U.S.)
4593	POPTHM_Log	Log of Population (U.S.)
4594	POPTHM_mva365	Population (U.S.) 365 Day MA
4595	POPTHM_mva365	Population (U.S.) 365 Day MA
4596	POPTHM_mva200	Population (U.S.) 200 Day MA
4597	POPTHM_mva200	Population (U.S.) 200 Day MA
4598	POPTHM_mva050	Population (U.S.) 50 Day MA
4599	POPTHM_mva050	Population (U.S.) 50 Day MA
4612	POPTHM.1_Log	Log of
4613	POPTHM.1_Log	Log of
4614	POPTHM.1_mva365	365 Day MA
4615	POPTHM.1_mva365	365 Day MA
4616	POPTHM.1_mva200	200 Day MA
4617	POPTHM.1_mva200	200 Day MA
4618	POPTHM.1_mva050	50 Day MA
4619	POPTHM.1_mva050	50 Day MA
4620	CLF16OV_YoY	Civilian Labor Force Level, SA Year over Year
4626	CLF16OV_Log	Log of Civilian Labor Force Level, SA
4627	CLF16OV_mva365	Civilian Labor Force Level, SA 365 Day MA
4628	CLF16OV_mva200	Civilian Labor Force Level, SA 200 Day MA
4629	CLF16OV_mva050	Civilian Labor Force Level, SA 50 Day MA
4635	LNU01000000_SmoothDer	Derivative of Smoothed Civilian Labor Force Level, NSA
4637	LNU01000000_mva365	Civilian Labor Force Level, NSA 365 Day MA
4638	LNU01000000_mva200	Civilian Labor Force Level, NSA 200 Day MA
4639	LNU01000000_mva050	Civilian Labor Force Level, NSA 50 Day MA
4648	LNU03000000_mva200	Unemployment Level (NSA) 200 Day MA
4650	UNEMPLOY_YoY	Unemployment Level, seasonally adjusted Year over Year
4653	UNEMPLOY_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Unemployment Level, seasonally adjusted
4655	UNEMPLOY_SmoothDer	Derivative of Smoothed Unemployment Level, seasonally adjusted
4656	UNEMPLOY_Log	Log of Unemployment Level, seasonally adjusted
4658	UNEMPLOY_mva200	Unemployment Level, seasonally adjusted 200 Day MA
4659	UNEMPLOY_mva050	Unemployment Level, seasonally adjusted 50 Day MA
4663	RSAFS_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Advance Retail Sales: Retail and Food Services
4665	RSAFS_SmoothDer	Derivative of Smoothed Advance Retail Sales: Retail and Food Services
4667	RSAFS_mva365	Advance Retail Sales: Retail and Food Services 365 Day MA
4668	RSAFS_mva200	Advance Retail Sales: Retail and Food Services 200 Day MA
4673	FRGSHPUSM649NCIS_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Cass Freight Index: Shipments
4675	FRGSHPUSM649NCIS_SmoothDer	Derivative of Smoothed Cass Freight Index: Shipments
4679	FRGSHPUSM649NCIS_mva050	Cass Freight Index: Shipments 50 Day MA
4680	BOPGTB_YoY	Trade Balance: Goods, Balance of Payments Basis (SA) Year over Year
4681	BOPGTB_YoY4	Trade Balance: Goods, Balance of Payments Basis (SA) 4 Year over 4 Year
4686	BOPGTB_Log	Log of Trade Balance: Goods, Balance of Payments Basis (SA)
4691	TERMCBPER24NS_YoY4	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 4 Year over 4 Year
4692	TERMCBPER24NS_YoY5	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 5 Year over 5 Year
4696	TERMCBPER24NS_Log	Log of Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan
4697	TERMCBPER24NS_mva365	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 365 Day MA
4698	TERMCBPER24NS_mva200	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 200 Day MA
4699	TERMCBPER24NS_mva050	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 50 Day MA
4706	A065RC1A027NBEA_Log	Log of Personal income (NSA)
4707	A065RC1A027NBEA_mva365	Personal income (NSA) 365 Day MA
4708	A065RC1A027NBEA_mva200	Personal income (NSA) 200 Day MA
4709	A065RC1A027NBEA_mva050	Personal income (NSA) 50 Day MA
4716	PI_Log	Log of Personal income (SA)
4717	PI_mva365	Personal income (SA) 365 Day MA
4718	PI_mva200	Personal income (SA) 200 Day MA
4719	PI_mva050	Personal income (SA) 50 Day MA
4726	PCE_Log	Log of Personal Consumption Expenditures (SA)
4727	PCE_mva365	Personal Consumption Expenditures (SA) 365 Day MA
4728	PCE_mva200	Personal Consumption Expenditures (SA) 200 Day MA
4729	PCE_mva050	Personal Consumption Expenditures (SA) 50 Day MA
4735	A053RC1Q027SBEA_SmoothDer	Derivative of Smoothed National income: Corporate profits before tax (without IVA and CCAdj)
4745	CPROFIT_SmoothDer	Derivative of Smoothed Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj)
4753	SPY.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4755	SPY.Open_SmoothDer	Derivative of Smoothed
4758	SPY.Open_mva200	200 Day MA
4759	SPY.Open_mva050	50 Day MA
4763	SPY.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4765	SPY.High_SmoothDer	Derivative of Smoothed
4768	SPY.High_mva200	200 Day MA
4769	SPY.High_mva050	50 Day MA
4773	SPY.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4775	SPY.Low_SmoothDer	Derivative of Smoothed
4778	SPY.Low_mva200	200 Day MA
4779	SPY.Low_mva050	50 Day MA
4783	SPY.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4785	SPY.Close_SmoothDer	Derivative of Smoothed
4788	SPY.Close_mva200	200 Day MA
4789	SPY.Close_mva050	50 Day MA
4803	SPY.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4805	SPY.Adjusted_SmoothDer	Derivative of Smoothed
4808	SPY.Adjusted_mva200	200 Day MA
4809	SPY.Adjusted_mva050	50 Day MA
4818	MDY.Open_mva200	200 Day MA
4853	MDY.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4855	MDY.Volume_SmoothDer	Derivative of Smoothed
4868	MDY.Adjusted_mva200	200 Day MA
4916	EES.Volume_Log	Log of
4973	IJR.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4975	IJR.Volume_SmoothDer	Derivative of Smoothed
4993	VGSTX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4995	VGSTX.Open_SmoothDer	Derivative of Smoothed
5003	VGSTX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5005	VGSTX.High_SmoothDer	Derivative of Smoothed
5013	VGSTX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5015	VGSTX.Low_SmoothDer	Derivative of Smoothed
5023	VGSTX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5025	VGSTX.Close_SmoothDer	Derivative of Smoothed
5030	VGSTX.Volume_YoY	Year over Year
5031	VGSTX.Volume_YoY4	4 Year over 4 Year
5032	VGSTX.Volume_YoY5	5 Year over 5 Year
5033	VGSTX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5034	VGSTX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
5035	VGSTX.Volume_SmoothDer	Derivative of Smoothed
5036	VGSTX.Volume_Log	Log of
5037	VGSTX.Volume_mva365	365 Day MA
5038	VGSTX.Volume_mva200	200 Day MA
5039	VGSTX.Volume_mva050	50 Day MA
5043	VGSTX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5048	VGSTX.Adjusted_mva200	200 Day MA
5049	VGSTX.Adjusted_mva050	50 Day MA
5053	VFINX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5055	VFINX.Open_SmoothDer	Derivative of Smoothed
5058	VFINX.Open_mva200	200 Day MA
5059	VFINX.Open_mva050	50 Day MA
5063	VFINX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5065	VFINX.High_SmoothDer	Derivative of Smoothed
5068	VFINX.High_mva200	200 Day MA
5069	VFINX.High_mva050	50 Day MA
5073	VFINX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5075	VFINX.Low_SmoothDer	Derivative of Smoothed
5078	VFINX.Low_mva200	200 Day MA
5079	VFINX.Low_mva050	50 Day MA
5083	VFINX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5085	VFINX.Close_SmoothDer	Derivative of Smoothed
5088	VFINX.Close_mva200	200 Day MA
5089	VFINX.Close_mva050	50 Day MA
5090	VFINX.Volume_YoY	Year over Year
5091	VFINX.Volume_YoY4	4 Year over 4 Year
5092	VFINX.Volume_YoY5	5 Year over 5 Year
5093	VFINX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5094	VFINX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
5095	VFINX.Volume_SmoothDer	Derivative of Smoothed
5096	VFINX.Volume_Log	Log of
5097	VFINX.Volume_mva365	365 Day MA
5098	VFINX.Volume_mva200	200 Day MA
5099	VFINX.Volume_mva050	50 Day MA
5103	VFINX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5105	VFINX.Adjusted_SmoothDer	Derivative of Smoothed
5108	VFINX.Adjusted_mva200	200 Day MA
5109	VFINX.Adjusted_mva050	50 Day MA
5173	VOT.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5178	VOT.Open_mva200	200 Day MA
5183	VOT.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5188	VOT.High_mva200	200 Day MA
5193	VOT.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5198	VOT.Low_mva200	200 Day MA
5203	VOT.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5208	VOT.Close_mva200	200 Day MA
5215	VOT.Volume_SmoothDer	Derivative of Smoothed
5223	VOT.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5228	VOT.Adjusted_mva200	200 Day MA
5230	TMFGX.Open_YoY	Year over Year
5232	TMFGX.Open_YoY5	5 Year over 5 Year
5236	TMFGX.Open_Log	Log of
5237	TMFGX.Open_mva365	365 Day MA
5238	TMFGX.Open_mva200	200 Day MA
5239	TMFGX.Open_mva050	50 Day MA
5240	TMFGX.High_YoY	Year over Year
5242	TMFGX.High_YoY5	5 Year over 5 Year
5246	TMFGX.High_Log	Log of
5247	TMFGX.High_mva365	365 Day MA
5248	TMFGX.High_mva200	200 Day MA
5249	TMFGX.High_mva050	50 Day MA
5250	TMFGX.Low_YoY	Year over Year
5252	TMFGX.Low_YoY5	5 Year over 5 Year
5256	TMFGX.Low_Log	Log of
5257	TMFGX.Low_mva365	365 Day MA
5258	TMFGX.Low_mva200	200 Day MA
5259	TMFGX.Low_mva050	50 Day MA
5260	TMFGX.Close_YoY	Year over Year
5262	TMFGX.Close_YoY5	5 Year over 5 Year
5266	TMFGX.Close_Log	Log of
5267	TMFGX.Close_mva365	365 Day MA
5268	TMFGX.Close_mva200	200 Day MA
5269	TMFGX.Close_mva050	50 Day MA
5270	TMFGX.Volume_YoY	Year over Year
5271	TMFGX.Volume_YoY4	4 Year over 4 Year
5272	TMFGX.Volume_YoY5	5 Year over 5 Year
5273	TMFGX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5274	TMFGX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
5275	TMFGX.Volume_SmoothDer	Derivative of Smoothed
5276	TMFGX.Volume_Log	Log of
5277	TMFGX.Volume_mva365	365 Day MA
5278	TMFGX.Volume_mva200	200 Day MA
5279	TMFGX.Volume_mva050	50 Day MA
5280	TMFGX.Adjusted_YoY	Year over Year
5282	TMFGX.Adjusted_YoY5	5 Year over 5 Year
5286	TMFGX.Adjusted_Log	Log of
5287	TMFGX.Adjusted_mva365	365 Day MA
5288	TMFGX.Adjusted_mva200	200 Day MA
5289	TMFGX.Adjusted_mva050	50 Day MA
5333	IWM.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5335	IWM.Volume_SmoothDer	Derivative of Smoothed
5338	IWM.Volume_mva200	200 Day MA
5353	ONEQ.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5355	ONEQ.Open_SmoothDer	Derivative of Smoothed
5358	ONEQ.Open_mva200	200 Day MA
5359	ONEQ.Open_mva050	50 Day MA
5363	ONEQ.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5365	ONEQ.High_SmoothDer	Derivative of Smoothed
5368	ONEQ.High_mva200	200 Day MA
5369	ONEQ.High_mva050	50 Day MA
5373	ONEQ.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5375	ONEQ.Low_SmoothDer	Derivative of Smoothed
5378	ONEQ.Low_mva200	200 Day MA
5379	ONEQ.Low_mva050	50 Day MA
5383	ONEQ.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5385	ONEQ.Close_SmoothDer	Derivative of Smoothed
5388	ONEQ.Close_mva200	200 Day MA
5389	ONEQ.Close_mva050	50 Day MA
5395	ONEQ.Volume_SmoothDer	Derivative of Smoothed
5403	ONEQ.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5405	ONEQ.Adjusted_SmoothDer	Derivative of Smoothed
5408	ONEQ.Adjusted_mva200	200 Day MA
5409	ONEQ.Adjusted_mva050	50 Day MA
5413	FSMAX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5423	FSMAX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5433	FSMAX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5443	FSMAX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5450	FSMAX.Volume_YoY	Year over Year
5451	FSMAX.Volume_YoY4	4 Year over 4 Year
5452	FSMAX.Volume_YoY5	5 Year over 5 Year
5453	FSMAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5454	FSMAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
5455	FSMAX.Volume_SmoothDer	Derivative of Smoothed
5456	FSMAX.Volume_Log	Log of
5457	FSMAX.Volume_mva365	365 Day MA
5458	FSMAX.Volume_mva200	200 Day MA
5459	FSMAX.Volume_mva050	50 Day MA
5463	FSMAX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5468	FSMAX.Adjusted_mva200	200 Day MA
5510	FXNAX.Volume_YoY	Year over Year
5511	FXNAX.Volume_YoY4	4 Year over 4 Year
5512	FXNAX.Volume_YoY5	5 Year over 5 Year
5513	FXNAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5514	FXNAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
5515	FXNAX.Volume_SmoothDer	Derivative of Smoothed
5516	FXNAX.Volume_Log	Log of
5517	FXNAX.Volume_mva365	365 Day MA
5518	FXNAX.Volume_mva200	200 Day MA
5519	FXNAX.Volume_mva050	50 Day MA
5528	FXNAX.Adjusted_mva200	200 Day MA
5538	HAINX.Open_mva200	200 Day MA
5539	HAINX.Open_mva050	50 Day MA
5548	HAINX.High_mva200	200 Day MA
5549	HAINX.High_mva050	50 Day MA
5558	HAINX.Low_mva200	200 Day MA
5559	HAINX.Low_mva050	50 Day MA
5568	HAINX.Close_mva200	200 Day MA
5569	HAINX.Close_mva050	50 Day MA
5570	HAINX.Volume_YoY	Year over Year
5571	HAINX.Volume_YoY4	4 Year over 4 Year
5572	HAINX.Volume_YoY5	5 Year over 5 Year
5573	HAINX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5574	HAINX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
5575	HAINX.Volume_SmoothDer	Derivative of Smoothed
5576	HAINX.Volume_Log	Log of
5577	HAINX.Volume_mva365	365 Day MA
5578	HAINX.Volume_mva200	200 Day MA
5579	HAINX.Volume_mva050	50 Day MA
5587	HAINX.Adjusted_mva365	365 Day MA
5588	HAINX.Adjusted_mva200	200 Day MA
5589	HAINX.Adjusted_mva050	50 Day MA
5593	HNACX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5595	HNACX.Open_SmoothDer	Derivative of Smoothed
5598	HNACX.Open_mva200	200 Day MA
5599	HNACX.Open_mva050	50 Day MA
5603	HNACX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5605	HNACX.High_SmoothDer	Derivative of Smoothed
5608	HNACX.High_mva200	200 Day MA
5609	HNACX.High_mva050	50 Day MA
5613	HNACX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5615	HNACX.Low_SmoothDer	Derivative of Smoothed
5618	HNACX.Low_mva200	200 Day MA
5619	HNACX.Low_mva050	50 Day MA
5623	HNACX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5625	HNACX.Close_SmoothDer	Derivative of Smoothed
5628	HNACX.Close_mva200	200 Day MA
5629	HNACX.Close_mva050	50 Day MA
5630	HNACX.Volume_YoY	Year over Year
5631	HNACX.Volume_YoY4	4 Year over 4 Year
5632	HNACX.Volume_YoY5	5 Year over 5 Year
5633	HNACX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5634	HNACX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
5635	HNACX.Volume_SmoothDer	Derivative of Smoothed
5636	HNACX.Volume_Log	Log of
5637	HNACX.Volume_mva365	365 Day MA
5638	HNACX.Volume_mva200	200 Day MA
5639	HNACX.Volume_mva050	50 Day MA
5643	HNACX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5645	HNACX.Adjusted_SmoothDer	Derivative of Smoothed
5648	HNACX.Adjusted_mva200	200 Day MA
5649	HNACX.Adjusted_mva050	50 Day MA
5658	VEU.Open_mva200	200 Day MA
5659	VEU.Open_mva050	50 Day MA
5668	VEU.High_mva200	200 Day MA
5669	VEU.High_mva050	50 Day MA
5678	VEU.Low_mva200	200 Day MA
5679	VEU.Low_mva050	50 Day MA
5688	VEU.Close_mva200	200 Day MA
5689	VEU.Close_mva050	50 Day MA
5708	VEU.Adjusted_mva200	200 Day MA
5709	VEU.Adjusted_mva050	50 Day MA
5750	VEIRX.Volume_YoY	Year over Year
5751	VEIRX.Volume_YoY4	4 Year over 4 Year
5752	VEIRX.Volume_YoY5	5 Year over 5 Year
5753	VEIRX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5754	VEIRX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
5755	VEIRX.Volume_SmoothDer	Derivative of Smoothed
5756	VEIRX.Volume_Log	Log of
5757	VEIRX.Volume_mva365	365 Day MA
5758	VEIRX.Volume_mva200	200 Day MA
5759	VEIRX.Volume_mva050	50 Day MA
5777	BIL.Open_mva365	365 Day MA
5787	BIL.High_mva365	365 Day MA
5797	BIL.Low_mva365	365 Day MA
5807	BIL.Close_mva365	365 Day MA
5823	BIL.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5825	BIL.Adjusted_SmoothDer	Derivative of Smoothed
5826	BIL.Adjusted_Log	Log of
5827	BIL.Adjusted_mva365	365 Day MA
5828	BIL.Adjusted_mva200	200 Day MA
5829	BIL.Adjusted_mva050	50 Day MA
5838	IVOO.Open_mva200	200 Day MA
5868	IVOO.Close_mva200	200 Day MA
5876	IVOO.Volume_Log	Log of
5888	IVOO.Adjusted_mva200	200 Day MA
5898	VO.Open_mva200	200 Day MA
5918	VO.Low_mva200	200 Day MA
5933	VO.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5935	VO.Volume_SmoothDer	Derivative of Smoothed
5936	VO.Volume_Log	Log of
5948	VO.Adjusted_mva200	200 Day MA
5995	CZA.Volume_SmoothDer	Derivative of Smoothed
5996	CZA.Volume_Log	Log of
6055	VYM.Volume_SmoothDer	Derivative of Smoothed
6073	ACWI.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6078	ACWI.Open_mva200	200 Day MA
6079	ACWI.Open_mva050	50 Day MA
6083	ACWI.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6088	ACWI.High_mva200	200 Day MA
6089	ACWI.High_mva050	50 Day MA
6093	ACWI.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6098	ACWI.Low_mva200	200 Day MA
6099	ACWI.Low_mva050	50 Day MA
6103	ACWI.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6108	ACWI.Close_mva200	200 Day MA
6109	ACWI.Close_mva050	50 Day MA
6123	ACWI.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6128	ACWI.Adjusted_mva200	200 Day MA
6129	ACWI.Adjusted_mva050	50 Day MA
6176	SLY.Volume_Log	Log of
6193	QQQ.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6195	QQQ.Open_SmoothDer	Derivative of Smoothed
6198	QQQ.Open_mva200	200 Day MA
6199	QQQ.Open_mva050	50 Day MA
6203	QQQ.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6205	QQQ.High_SmoothDer	Derivative of Smoothed
6208	QQQ.High_mva200	200 Day MA
6209	QQQ.High_mva050	50 Day MA
6213	QQQ.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6215	QQQ.Low_SmoothDer	Derivative of Smoothed
6218	QQQ.Low_mva200	200 Day MA
6219	QQQ.Low_mva050	50 Day MA
6223	QQQ.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6225	QQQ.Close_SmoothDer	Derivative of Smoothed
6228	QQQ.Close_mva200	200 Day MA
6229	QQQ.Close_mva050	50 Day MA
6233	QQQ.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6235	QQQ.Volume_SmoothDer	Derivative of Smoothed
6243	QQQ.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6245	QQQ.Adjusted_SmoothDer	Derivative of Smoothed
6248	QQQ.Adjusted_mva200	200 Day MA
6249	QQQ.Adjusted_mva050	50 Day MA
6260	HYMB.High_YoY	Year over Year
6296	HYMB.Volume_Log	Log of
6308	HYMB.Adjusted_mva200	200 Day MA
6316	GOLD.Open_Log	Log of
6356	GOLD.Volume_Log	Log of
6375	BKR.Open_SmoothDer	Derivative of Smoothed
6376	BKR.Open_Log	Log of
6385	BKR.High_SmoothDer	Derivative of Smoothed
6395	BKR.Low_SmoothDer	Derivative of Smoothed
6413	BKR.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6415	BKR.Volume_SmoothDer	Derivative of Smoothed
6416	BKR.Volume_Log	Log of
6428	BKR.Adjusted_mva200	200 Day MA
6437	SLB.Open_mva365	365 Day MA
6447	SLB.High_mva365	365 Day MA
6457	SLB.Low_mva365	365 Day MA
6467	SLB.Close_mva365	365 Day MA
6487	SLB.Adjusted_mva365	365 Day MA
6496	HAL.Open_Log	Log of
6533	HAL.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6535	HAL.Volume_SmoothDer	Derivative of Smoothed
6536	HAL.Volume_Log	Log of
6555	IP.Open_SmoothDer	Derivative of Smoothed
6556	IP.Open_Log	Log of
6565	IP.High_SmoothDer	Derivative of Smoothed
6575	IP.Low_SmoothDer	Derivative of Smoothed
6585	IP.Close_SmoothDer	Derivative of Smoothed
6593	IP.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6595	IP.Volume_SmoothDer	Derivative of Smoothed
6605	IP.Adjusted_SmoothDer	Derivative of Smoothed
6615	PKG.Open_SmoothDer	Derivative of Smoothed
6625	PKG.High_SmoothDer	Derivative of Smoothed
6635	PKG.Low_SmoothDer	Derivative of Smoothed
6645	PKG.Close_SmoothDer	Derivative of Smoothed
6665	PKG.Adjusted_SmoothDer	Derivative of Smoothed
6713	UPS.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6715	UPS.Volume_SmoothDer	Derivative of Smoothed
6735	FDX.Open_SmoothDer	Derivative of Smoothed
6738	FDX.Open_mva200	200 Day MA
6745	FDX.High_SmoothDer	Derivative of Smoothed
6748	FDX.High_mva200	200 Day MA
6755	FDX.Low_SmoothDer	Derivative of Smoothed
6758	FDX.Low_mva200	200 Day MA
6765	FDX.Close_SmoothDer	Derivative of Smoothed
6768	FDX.Close_mva200	200 Day MA
6770	FDX.Volume_YoY	Year over Year
6773	FDX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6774	FDX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
6775	FDX.Volume_SmoothDer	Derivative of Smoothed
6785	FDX.Adjusted_SmoothDer	Derivative of Smoothed
6788	FDX.Adjusted_mva200	200 Day MA
6796	T.Open_Log	Log of
6833	T.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6835	T.Volume_SmoothDer	Derivative of Smoothed
6855	VZ.Open_SmoothDer	Derivative of Smoothed
6865	VZ.High_SmoothDer	Derivative of Smoothed
6875	VZ.Low_SmoothDer	Derivative of Smoothed
6885	VZ.Close_SmoothDer	Derivative of Smoothed
6893	VZ.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6905	VZ.Adjusted_SmoothDer	Derivative of Smoothed
6910	ISMMANPMI_YoY	Institute of Supply Managment PMI Composite Index Year over Year
6911	ISMMANPMI_YoY4	Institute of Supply Managment PMI Composite Index 4 Year over 4 Year
6916	ISMMANPMI_Log	Log of Institute of Supply Managment PMI Composite Index
6918	ISMMANPMI_mva200	Institute of Supply Managment PMI Composite Index 200 Day MA
6919	ISMMANPMI_mva050	Institute of Supply Managment PMI Composite Index 50 Day MA
6920	MULTPLSP500PERATIOMONTH_YoY	S&P 500 TTM P/E Year over Year
6922	MULTPLSP500PERATIOMONTH_YoY5	S&P 500 TTM P/E 5 Year over 5 Year
6923	MULTPLSP500PERATIOMONTH_Smooth	Savitsky-Golay Smoothed (p=3, n=365) S&P 500 TTM P/E
6926	MULTPLSP500PERATIOMONTH_Log	Log of S&P 500 TTM P/E
6927	MULTPLSP500PERATIOMONTH_mva365	S&P 500 TTM P/E 365 Day MA
6928	MULTPLSP500PERATIOMONTH_mva200	S&P 500 TTM P/E 200 Day MA
6929	MULTPLSP500PERATIOMONTH_mva050	S&P 500 TTM P/E 50 Day MA
6936	MULTPLSP500SALESQUARTER_Log	Log of S&P 500 TTM Sales (Not Inflation Adjusted)
6937	MULTPLSP500SALESQUARTER_mva365	S&P 500 TTM Sales (Not Inflation Adjusted) 365 Day MA
6938	MULTPLSP500SALESQUARTER_mva200	S&P 500 TTM Sales (Not Inflation Adjusted) 200 Day MA
6939	MULTPLSP500SALESQUARTER_mva050	S&P 500 TTM Sales (Not Inflation Adjusted) 50 Day MA
6947	MULTPLSP500DIVYIELDMONTH_mva365	S&P 500 Dividend Yield by Month 365 Day MA
6956	MULTPLSP500DIVMONTH_Log	Log of S&P 500 Dividend by Month (Inflation Adjusted)
6957	MULTPLSP500DIVMONTH_mva365	S&P 500 Dividend by Month (Inflation Adjusted) 365 Day MA
6958	MULTPLSP500DIVMONTH_mva200	S&P 500 Dividend by Month (Inflation Adjusted) 200 Day MA
6959	MULTPLSP500DIVMONTH_mva050	S&P 500 Dividend by Month (Inflation Adjusted) 50 Day MA
6960	CHRISCMEHG1_YoY	Copper Futures, Continuous Contract #1 (HG1) (Front Month) Year over Year
6966	CHRISCMEHG1_Log	Log of Copper Futures, Continuous Contract #1 (HG1) (Front Month)
6967	CHRISCMEHG1_mva365	Copper Futures, Continuous Contract #1 (HG1) (Front Month) 365 Day MA
6968	CHRISCMEHG1_mva200	Copper Futures, Continuous Contract #1 (HG1) (Front Month) 200 Day MA
6969	CHRISCMEHG1_mva050	Copper Futures, Continuous Contract #1 (HG1) (Front Month) 50 Day MA
6970	WWDIWLDISAIRGOODMTK1_YoY	Air transport, freight Year over Year
6976	WWDIWLDISAIRGOODMTK1_Log	Log of Air transport, freight
6977	WWDIWLDISAIRGOODMTK1_mva365	Air transport, freight 365 Day MA
6978	WWDIWLDISAIRGOODMTK1_mva200	Air transport, freight 200 Day MA
6979	WWDIWLDISAIRGOODMTK1_mva050	Air transport, freight 50 Day MA
6985	LBMAGOLD.USD_AM_SmoothDer	Derivative of Smoothed
6987	LBMAGOLD.USD_AM_mva365	365 Day MA
6988	LBMAGOLD.USD_AM_mva200	200 Day MA
6995	LBMAGOLD.USD_PM_SmoothDer	Derivative of Smoothed
6997	LBMAGOLD.USD_PM_mva365	365 Day MA
6998	LBMAGOLD.USD_PM_mva200	200 Day MA
7005	LBMAGOLD.GBP_AM_SmoothDer	Derivative of Smoothed
7007	LBMAGOLD.GBP_AM_mva365	365 Day MA
7008	LBMAGOLD.GBP_AM_mva200	200 Day MA
7015	LBMAGOLD.GBP_PM_SmoothDer	Derivative of Smoothed
7017	LBMAGOLD.GBP_PM_mva365	365 Day MA
7018	LBMAGOLD.GBP_PM_mva200	200 Day MA
7023	LBMAGOLD.EURO_AM_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
7025	LBMAGOLD.EURO_AM_SmoothDer	Derivative of Smoothed
7027	LBMAGOLD.EURO_AM_mva365	365 Day MA
7028	LBMAGOLD.EURO_AM_mva200	200 Day MA
7033	LBMAGOLD.EURO_PM_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
7035	LBMAGOLD.EURO_PM_SmoothDer	Derivative of Smoothed
7037	LBMAGOLD.EURO_PM_mva365	365 Day MA
7038	LBMAGOLD.EURO_PM_mva200	200 Day MA
7046	PETA103600001M_Log	Log of U.S. Total Gasoline Retail Sales by Refiners, Monthly
7048	PETA103600001M_mva200	U.S. Total Gasoline Retail Sales by Refiners, Monthly 200 Day MA
7049	PETA103600001M_mva050	U.S. Total Gasoline Retail Sales by Refiners, Monthly 50 Day MA
7055	PETA123600001M_SmoothDer	Derivative of Smoothed U.S. Regular Gasoline Retail Sales by Refiners, Monthly
7056	PETA123600001M_Log	Log of U.S. Regular Gasoline Retail Sales by Refiners, Monthly
7057	PETA123600001M_mva365	U.S. Regular Gasoline Retail Sales by Refiners, Monthly 365 Day MA
7058	PETA123600001M_mva200	U.S. Regular Gasoline Retail Sales by Refiners, Monthly 200 Day MA
7059	PETA123600001M_mva050	U.S. Regular Gasoline Retail Sales by Refiners, Monthly 50 Day MA
7060	PETA143B00001M_YoY	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly Year over Year
7061	PETA143B00001M_YoY4	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 4 Year over 4 Year
7062	PETA143B00001M_YoY5	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 5 Year over 5 Year
7066	PETA143B00001M_Log	Log of U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly
7067	PETA143B00001M_mva365	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 365 Day MA
7068	PETA143B00001M_mva200	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 200 Day MA
7069	PETA143B00001M_mva050	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 50 Day MA
7070	PETA133B00001M_YoY	U.S. Premium Gasoline Bulk Sales (Volume) by Refiners, Monthly Year over Year
7076	PETA133B00001M_Log	Log of U.S. Premium Gasoline Bulk Sales (Volume) by Refiners, Monthly
7078	PETA133B00001M_mva200	U.S. Premium Gasoline Bulk Sales (Volume) by Refiners, Monthly 200 Day MA
7079	PETA133B00001M_mva050	U.S. Premium Gasoline Bulk Sales (Volume) by Refiners, Monthly 50 Day MA
7081	TOTALOGNRPUSM_YoY4	Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly 4 Year over 4 Year
7085	TOTALOGNRPUSM_SmoothDer	Derivative of Smoothed Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly
7087	TOTALOGNRPUSM_mva365	Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly 365 Day MA
7091	TOTALPANRPUSM_YoY4	Crude Oil Rotary Rigs in Operation, Monthly 4 Year over 4 Year
7101	TOTALNGNRPUSM_YoY4	Natural Gas Rotary Rigs in Operation, Monthly 4 Year over 4 Year
7103	TOTALNGNRPUSM_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Natural Gas Rotary Rigs in Operation, Monthly
7105	TOTALNGNRPUSM_SmoothDer	Derivative of Smoothed Natural Gas Rotary Rigs in Operation, Monthly
7106	TOTALNGNRPUSM_Log	Log of Natural Gas Rotary Rigs in Operation, Monthly
7107	TOTALNGNRPUSM_mva365	Natural Gas Rotary Rigs in Operation, Monthly 365 Day MA
7109	TOTALNGNRPUSM_mva050	Natural Gas Rotary Rigs in Operation, Monthly 50 Day MA
7111	BKRTotal_YoY4	Total Rig Count 4 Year over 4 Year
7115	BKRTotal_SmoothDer	Derivative of Smoothed Total Rig Count
7116	BKRTotal_Log	Log of Total Rig Count
7117	BKRTotal_mva365	Total Rig Count 365 Day MA
7118	BKRTotal_mva200	Total Rig Count 200 Day MA
7119	BKRTotal_mva050	Total Rig Count 50 Day MA
7121	BKRGas_YoY4	Gas Rig Count 4 Year over 4 Year
7125	BKRGas_SmoothDer	Derivative of Smoothed Gas Rig Count
7126	BKRGas_Log	Log of Gas Rig Count
7127	BKRGas_mva365	Gas Rig Count 365 Day MA
7128	BKRGas_mva200	Gas Rig Count 200 Day MA
7129	BKRGas_mva050	Gas Rig Count 50 Day MA
7135	BKROil_SmoothDer	Derivative of Smoothed Oil Rig Count
7136	BKROil_Log	Log of Oil Rig Count
7137	BKROil_mva365	Oil Rig Count 365 Day MA
7138	BKROil_mva200	Oil Rig Count 200 Day MA
7139	BKROil_mva050	Oil Rig Count 50 Day MA
7140	FARMINCOME_YoY	Net Farm Income Year over Year
7146	FARMINCOME_Log	Log of Net Farm Income
7147	FARMINCOME_mva365	Net Farm Income 365 Day MA
7148	FARMINCOME_mva200	Net Farm Income 200 Day MA
7149	FARMINCOME_mva050	Net Farm Income 50 Day MA
7150	OPEARNINGSPERSHARE_YoY	Operating Earnings per Share Year over Year
7153	OPEARNINGSPERSHARE_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Operating Earnings per Share
7156	OPEARNINGSPERSHARE_Log	Log of Operating Earnings per Share
7158	OPEARNINGSPERSHARE_mva200	Operating Earnings per Share 200 Day MA
7159	OPEARNINGSPERSHARE_mva050	Operating Earnings per Share 50 Day MA
7160	AREARNINGSPERSHARE_YoY	As-Reported Earnings per Share Year over Year
7166	AREARNINGSPERSHARE_Log	Log of As-Reported Earnings per Share
7176	CASHDIVIDENDSPERSHR_Log	Log of Cash Dividends per Share
7177	CASHDIVIDENDSPERSHR_mva365	Cash Dividends per Share 365 Day MA
7178	CASHDIVIDENDSPERSHR_mva200	Cash Dividends per Share 200 Day MA
7179	CASHDIVIDENDSPERSHR_mva050	Cash Dividends per Share 50 Day MA
7186	SALESPERSHR_Log	Log of Sales per Share
7187	SALESPERSHR_mva365	Sales per Share 365 Day MA
7188	SALESPERSHR_mva200	Sales per Share 200 Day MA
7189	SALESPERSHR_mva050	Sales per Share 50 Day MA
7190	BOOKVALPERSHR_YoY	Book value per Share Year over Year
7196	BOOKVALPERSHR_Log	Log of Book value per Share
7197	BOOKVALPERSHR_mva365	Book value per Share 365 Day MA
7198	BOOKVALPERSHR_mva200	Book value per Share 200 Day MA
7199	BOOKVALPERSHR_mva050	Book value per Share 50 Day MA
7206	CAPEXPERSHR_Log	Log of Cap ex per Share
7207	CAPEXPERSHR_mva365	Cap ex per Share 365 Day MA
7208	CAPEXPERSHR_mva200	Cap ex per Share 200 Day MA
7209	CAPEXPERSHR_mva050	Cap ex per Share 50 Day MA
7210	PRICE_YoY	Price Year over Year
7216	PRICE_Log	Log of Price
7218	PRICE_mva200	Price 200 Day MA
7219	PRICE_mva050	Price 50 Day MA
7220	OPEARNINGSTTM_YoY	TTM Operating Earnings Year over Year
7225	OPEARNINGSTTM_SmoothDer	Derivative of Smoothed TTM Operating Earnings
7230	AREARNINGSTTM_YoY	TTM Reported Earnings Year over Year
7235	AREARNINGSTTM_SmoothDer	Derivative of Smoothed TTM Reported Earnings
7240	FINRAMarginDebt_YoY	Margin Debt Year over Year
7243	FINRAMarginDebt_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Margin Debt
7245	FINRAMarginDebt_SmoothDer	Derivative of Smoothed Margin Debt
7246	FINRAMarginDebt_Log	Log of Margin Debt
7249	FINRAMarginDebt_mva050	Margin Debt 50 Day MA
7255	FINRAFreeCreditMargin_SmoothDer	Derivative of Smoothed Free Credit Balances in Customers’ Securities Margin Accounts
7260	OCCEquityVolume_YoY	Equity Options Volume Year over Year
7266	OCCEquityVolume_Log	Log of Equity Options Volume
7267	OCCEquityVolume_mva365	Equity Options Volume 365 Day MA
7268	OCCEquityVolume_mva200	Equity Options Volume 200 Day MA
7269	OCCEquityVolume_mva050	Equity Options Volume 50 Day MA
7270	OCCNonEquityVolume_YoY	Non-Equity Options Volume Year over Year
7276	OCCNonEquityVolume_Log	Log of Non-Equity Options Volume
7277	OCCNonEquityVolume_mva365	Non-Equity Options Volume 365 Day MA
7278	OCCNonEquityVolume_mva200	Non-Equity Options Volume 200 Day MA
7279	OCCNonEquityVolume_mva050	Non-Equity Options Volume 50 Day MA
7285	RSALESAGG_SmoothDer	Derivative of Smoothed Real Retail and Food Services Sales (RRSFS and RSALES)
7296	BUSLOANS.minus.BUSLOANSNSA_Log	Log of Business Loans (Montlhy) SA - NSA
7306	BUSLOANS.minus.BUSLOANSNSA.by.GDP_Log	Log of Business Loans (Montlhy) SA - NSA divided by GDP
7327	BUSLOANS.INTEREST_mva365	Business Loans (Monthly, SA) Adjusted Interest Burdens 365 Day MA
7337	BUSLOANS.INTEREST.by.GDP_mva365	Business Loans (Monthly, SA) Adjusted Interest Burden Divided by GDP 365 Day MA
7347	BUSLOANSNSA.by.GDP_mva365	Business Loans Normalized by GDP 365 Day MA
7377	TOTCINSA.INTEREST_mva365	Business Loans (Weekly, NSA) Adjusted Interest Burdens 365 Day MA
7387	TOTCINSA.INTEREST.by.GDP_mva365	Business Loans (weekly, NSA) Adjusted Interest Burden Divided by GDP 365 Day MA
7391	W875RX1.by.GDP_YoY4	Real Personal Income Normalized by GDP 4 Year over 4 Year
7393	W875RX1.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Personal Income Normalized by GDP
7395	W875RX1.by.GDP_SmoothDer	Derivative of Smoothed Real Personal Income Normalized by GDP
7396	W875RX1.by.GDP_Log	Log of Real Personal Income Normalized by GDP
7400	A065RC1A027NBEA.by.GDP_YoY	Personal Income (NSA) Normalized by GDP Year over Year
7405	A065RC1A027NBEA.by.GDP_SmoothDer	Derivative of Smoothed Personal Income (NSA) Normalized by GDP
7411	PI.by.GDP_YoY4	Personal Income (SA) Normalized by GDP 4 Year over 4 Year
7413	PI.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Personal Income (SA) Normalized by GDP
7415	PI.by.GDP_SmoothDer	Derivative of Smoothed Personal Income (SA) Normalized by GDP
7416	PI.by.GDP_Log	Log of Personal Income (SA) Normalized by GDP
7418	PI.by.GDP_mva200	Personal Income (SA) Normalized by GDP 200 Day MA
7419	PI.by.GDP_mva050	Personal Income (SA) Normalized by GDP 50 Day MA
7420	A053RC1Q027SBEA.by.GDP_YoY	National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP Year over Year
7421	A053RC1Q027SBEA.by.GDP_YoY4	National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP 4 Year over 4 Year
7425	A053RC1Q027SBEA.by.GDP_SmoothDer	Derivative of Smoothed National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP
7435	CPROFIT.by.GDP_SmoothDer	Derivative of Smoothed National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP
7443	CONSUMERNSA.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans Not Seasonally Adjusted divided by GDP
7445	CONSUMERNSA.by.GDP_SmoothDer	Derivative of Smoothed Consumer Loans Not Seasonally Adjusted divided by GDP
7446	CONSUMERNSA.by.GDP_Log	Log of Consumer Loans Not Seasonally Adjusted divided by GDP
7447	CONSUMERNSA.by.GDP_mva365	Consumer Loans Not Seasonally Adjusted divided by GDP 365 Day MA
7449	CONSUMERNSA.by.GDP_mva050	Consumer Loans Not Seasonally Adjusted divided by GDP 50 Day MA
7453	RREACBM027NBOG.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Monthly, NSA) divided by GDP
7455	RREACBM027NBOG.by.GDP_SmoothDer	Derivative of Smoothed Residental Real Estate Loans (Monthly, NSA) divided by GDP
7456	RREACBM027NBOG.by.GDP_Log	Log of Residental Real Estate Loans (Monthly, NSA) divided by GDP
7457	RREACBM027NBOG.by.GDP_mva365	Residental Real Estate Loans (Monthly, NSA) divided by GDP 365 Day MA
7458	RREACBM027NBOG.by.GDP_mva200	Residental Real Estate Loans (Monthly, NSA) divided by GDP 200 Day MA
7459	RREACBM027NBOG.by.GDP_mva050	Residental Real Estate Loans (Monthly, NSA) divided by GDP 50 Day MA
7461	RREACBM027SBOG.by.GDP_YoY4	Residental Real Estate Loans (Monthly, SA) divided by GDP 4 Year over 4 Year
7462	RREACBM027SBOG.by.GDP_YoY5	Residental Real Estate Loans (Monthly, SA) divided by GDP 5 Year over 5 Year
7463	RREACBM027SBOG.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Monthly, SA) divided by GDP
7465	RREACBM027SBOG.by.GDP_SmoothDer	Derivative of Smoothed Residental Real Estate Loans (Monthly, SA) divided by GDP
7466	RREACBM027SBOG.by.GDP_Log	Log of Residental Real Estate Loans (Monthly, SA) divided by GDP
7467	RREACBM027SBOG.by.GDP_mva365	Residental Real Estate Loans (Monthly, SA) divided by GDP 365 Day MA
7468	RREACBM027SBOG.by.GDP_mva200	Residental Real Estate Loans (Monthly, SA) divided by GDP 200 Day MA
7469	RREACBM027SBOG.by.GDP_mva050	Residental Real Estate Loans (Monthly, SA) divided by GDP 50 Day MA
7473	RREACBW027SBOG.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Weekly, SA) divided by GDP
7475	RREACBW027SBOG.by.GDP_SmoothDer	Derivative of Smoothed Residental Real Estate Loans (Weekly, SA) divided by GDP
7477	RREACBW027SBOG.by.GDP_mva365	Residental Real Estate Loans (Weekly, SA) divided by GDP 365 Day MA
7478	RREACBW027SBOG.by.GDP_mva200	Residental Real Estate Loans (Weekly, SA) divided by GDP 200 Day MA
7483	RREACBW027NBOG.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Weekly, NSA) divided by GDP
7487	RREACBW027NBOG.by.GDP_mva365	Residental Real Estate Loans (Weekly, NSA) divided by GDP 365 Day MA
7488	RREACBW027NBOG.by.GDP_mva200	Residental Real Estate Loans (Weekly, NSA) divided by GDP 200 Day MA
7491	UMDMNO.by.GDP_YoY4	Durable Goods (Monthly, NSA) divided by GDP 4 Year over 4 Year
7495	UMDMNO.by.GDP_SmoothDer	Derivative of Smoothed Durable Goods (Monthly, NSA) divided by GDP
7502	DGORDER.by.GDP_YoY5	Durable Goods (Monthly, NSA) divided by GDP 5 Year over 5 Year
7503	DGORDER.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Durable Goods (Monthly, NSA) divided by GDP
7505	DGORDER.by.GDP_SmoothDer	Derivative of Smoothed Durable Goods (Monthly, NSA) divided by GDP
7506	DGORDER.by.GDP_Log	Log of Durable Goods (Monthly, NSA) divided by GDP
7509	DGORDER.by.GDP_mva050	Durable Goods (Monthly, NSA) divided by GDP 50 Day MA
7511	ASHMA.by.GDP_YoY4	Home Mortgages (Quarterly, NSA) divided by GDP 4 Year over 4 Year
7512	ASHMA.by.GDP_YoY5	Home Mortgages (Quarterly, NSA) divided by GDP 5 Year over 5 Year
7515	ASHMA.by.GDP_SmoothDer	Derivative of Smoothed Home Mortgages (Quarterly, NSA) divided by GDP
7523	ASHMA.INTEREST_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens
7527	ASHMA.INTEREST_mva365	Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 365 Day MA
7529	ASHMA.INTEREST_mva050	Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 50 Day MA
7533	ASHMA.INTEREST.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens Divided by GDP
7537	ASHMA.INTEREST.by.GDP_mva365	Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens Divided by GDP 365 Day MA
7539	ASHMA.INTEREST.by.GDP_mva050	Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens Divided by GDP 50 Day MA
7543	CONSUMERNSA.INTEREST_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans (Not Seasonally Adjusted) Interest Burdens
7546	CONSUMERNSA.INTEREST_Log	Log of Consumer Loans (Not Seasonally Adjusted) Interest Burdens
7547	CONSUMERNSA.INTEREST_mva365	Consumer Loans (Not Seasonally Adjusted) Interest Burdens 365 Day MA
7548	CONSUMERNSA.INTEREST_mva200	Consumer Loans (Not Seasonally Adjusted) Interest Burdens 200 Day MA
7549	CONSUMERNSA.INTEREST_mva050	Consumer Loans (Not Seasonally Adjusted) Interest Burdens 50 Day MA
7551	CONSUMERNSA.INTEREST.by.GDP_YoY4	Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 4 Year over 4 Year
7552	CONSUMERNSA.INTEREST.by.GDP_YoY5	Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 5 Year over 5 Year
7553	CONSUMERNSA.INTEREST.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
7556	CONSUMERNSA.INTEREST.by.GDP_Log	Log of Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
7557	CONSUMERNSA.INTEREST.by.GDP_mva365	Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 365 Day MA
7558	CONSUMERNSA.INTEREST.by.GDP_mva200	Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 200 Day MA
7559	CONSUMERNSA.INTEREST.by.GDP_mva050	Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 50 Day MA
7566	TOTLNNSA_Log	Log of Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA)
7567	TOTLNNSA_mva365	Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA) 365 Day MA
7568	TOTLNNSA_mva200	Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA) 200 Day MA
7569	TOTLNNSA_mva050	Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA) 50 Day MA
7573	TOTLNNSA.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Total Loans Not Seasonally Adjusted divided by GDP
7576	TOTLNNSA.by.GDP_Log	Log of Total Loans Not Seasonally Adjusted divided by GDP
7577	TOTLNNSA.by.GDP_mva365	Total Loans Not Seasonally Adjusted divided by GDP 365 Day MA
7578	TOTLNNSA.by.GDP_mva200	Total Loans Not Seasonally Adjusted divided by GDP 200 Day MA
7579	TOTLNNSA.by.GDP_mva050	Total Loans Not Seasonally Adjusted divided by GDP 50 Day MA
7587	TOTLNNSA.INTEREST_mva365	Total Loans Not Seasonally Adjusted Interest Burdens 365 Day MA
7597	TOTLNNSA.INTEREST.by.GDP_mva365	Total Loans Not Seasonally Adjusted Interest Burden Divided by GDP 365 Day MA
7600	WRESBAL.by.GDP_YoY	Reserve Balances with Federal Reserve Banks Divided by GDP Year over Year
7602	WRESBAL.by.GDP_YoY5	Reserve Balances with Federal Reserve Banks Divided by GDP 5 Year over 5 Year
7603	WRESBAL.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Reserve Balances with Federal Reserve Banks Divided by GDP
7605	WRESBAL.by.GDP_SmoothDer	Derivative of Smoothed Reserve Balances with Federal Reserve Banks Divided by GDP
7610	EXCSRESNW.by.GDP_YoY	Excess Reserves of Depository Institutions Divided by GDP Year over Year
7612	EXCSRESNW.by.GDP_YoY5	Excess Reserves of Depository Institutions Divided by GDP 5 Year over 5 Year
7615	EXCSRESNW.by.GDP_SmoothDer	Derivative of Smoothed Excess Reserves of Depository Institutions Divided by GDP
7623	WLRRAL.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
7625	WLRRAL.by.GDP_SmoothDer	Derivative of Smoothed Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
7635	SOFR99.minus.SOFR1_SmoothDer	Derivative of Smoothed Secured Overnight Financing Rate: 99th Percentile - 1st Percentile
7646	EXPCH.minus.IMPCH_Log	Log of U.S. Exports to China (FAS Basis) - U.S. Imports to China (Customs Basis)
7647	EXPCH.minus.IMPCH_mva365	U.S. Exports to China (FAS Basis) - U.S. Imports to China (Customs Basis) 365 Day MA
7648	EXPCH.minus.IMPCH_mva200	U.S. Exports to China (FAS Basis) - U.S. Imports to China (Customs Basis) 200 Day MA
7656	EXPMX.minus.IMPMX_Log	Log of
7660	SRPSABSNNCB.by.GDP_YoY	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP Year over Year
7661	SRPSABSNNCB.by.GDP_YoY4	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP 4 Year over 4 Year
7662	SRPSABSNNCB.by.GDP_YoY5	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP 5 Year over 5 Year
7666	SRPSABSNNCB.by.GDP_Log	Log of Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP
7668	SRPSABSNNCB.by.GDP_mva200	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP 200 Day MA
7669	SRPSABSNNCB.by.GDP_mva050	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP 50 Day MA
7671	ASTLL.by.GDP_YoY4	All sectors; total loans; liability, Level (NSA) Divided by GDP 4 Year over 4 Year
7676	ASTLL.by.GDP_Log	Log of All sectors; total loans; liability, Level (NSA) Divided by GDP
7677	ASTLL.by.GDP_mva365	All sectors; total loans; liability, Level (NSA) Divided by GDP 365 Day MA
7678	ASTLL.by.GDP_mva200	All sectors; total loans; liability, Level (NSA) Divided by GDP 200 Day MA
7679	ASTLL.by.GDP_mva050	All sectors; total loans; liability, Level (NSA) Divided by GDP 50 Day MA
7680	ASFMA.by.GDP_YoY	All sectors; farm mortgages; asset, Level (NSA) Divided by GDP Year over Year
7682	ASFMA.by.GDP_YoY5	All sectors; farm mortgages; asset, Level (NSA) Divided by GDP 5 Year over 5 Year
7686	ASFMA.by.GDP_Log	Log of All sectors; farm mortgages; asset, Level (NSA) Divided by GDP
7687	ASFMA.by.GDP_mva365	All sectors; farm mortgages; asset, Level (NSA) Divided by GDP 365 Day MA
7688	ASFMA.by.GDP_mva200	All sectors; farm mortgages; asset, Level (NSA) Divided by GDP 200 Day MA
7689	ASFMA.by.GDP_mva050	All sectors; farm mortgages; asset, Level (NSA) Divided by GDP 50 Day MA
7696	ASFMA.by.ASTLL_Log	Log of All sectors; total loans Divided by farm mortgages
7697	ASFMA.by.ASTLL_mva365	All sectors; total loans Divided by farm mortgages 365 Day MA
7698	ASFMA.by.ASTLL_mva200	All sectors; total loans Divided by farm mortgages 200 Day MA
7699	ASFMA.by.ASTLL_mva050	All sectors; total loans Divided by farm mortgages 50 Day MA
7703	ASFMA.INTEREST_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens
7707	ASFMA.INTEREST_mva365	Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 365 Day MA
7709	ASFMA.INTEREST_mva050	Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 50 Day MA
7713	ASFMA.INTEREST.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Farm Mortgages (Quarterly, NSA) Interest Burden Divided by GDP
7717	ASFMA.INTEREST.by.GDP_mva365	Farm Mortgages (Quarterly, NSA) Interest Burden Divided by GDP 365 Day MA
7719	ASFMA.INTEREST.by.GDP_mva050	Farm Mortgages (Quarterly, NSA) Interest Burden Divided by GDP 50 Day MA
7720	FARMINCOME.by.GDP_YoY	Farm Income (Annual, NSA) Divided by GDP Year over Year
7721	FARMINCOME.by.GDP_YoY4	Farm Income (Annual, NSA) Divided by GDP 4 Year over 4 Year
7725	FARMINCOME.by.GDP_SmoothDer	Derivative of Smoothed Farm Income (Annual, NSA) Divided by GDP
7730	BOGMBASE.by.GDP_YoY	BOGMBASE Divided by GDP Year over Year
7731	BOGMBASE.by.GDP_YoY4	BOGMBASE Divided by GDP 4 Year over 4 Year
7732	BOGMBASE.by.GDP_YoY5	BOGMBASE Divided by GDP 5 Year over 5 Year
7733	BOGMBASE.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) BOGMBASE Divided by GDP
7735	BOGMBASE.by.GDP_SmoothDer	Derivative of Smoothed BOGMBASE Divided by GDP
7736	BOGMBASE.by.GDP_Log	Log of BOGMBASE Divided by GDP
7739	BOGMBASE.by.GDP_mva050	BOGMBASE Divided by GDP 50 Day MA
7741	WALCL.by.GDP_YoY4	All Federal Reserve Banks: Total Assets Divided by GDP 4 Year over 4 Year
7745	WALCL.by.GDP_SmoothDer	Derivative of Smoothed All Federal Reserve Banks: Total Assets Divided by GDP
7750	ECBASSETS.by.EUNNGDP_YoY	Central Bank Assets for Euro Area (11-19 Countries) Divided by GDP Year over Year
7751	ECBASSETS.by.EUNNGDP_YoY4	Central Bank Assets for Euro Area (11-19 Countries) Divided by GDP 4 Year over 4 Year
7755	ECBASSETS.by.EUNNGDP_SmoothDer	Derivative of Smoothed Central Bank Assets for Euro Area (11-19 Countries) Divided by GDP
7763	DGS30TO10_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Yield Curve, 30 and 10 Year Treasury (DGS30-DGS10)
7765	DGS30TO10_SmoothDer	Derivative of Smoothed Yield Curve, 30 and 10 Year Treasury (DGS30-DGS10)
7766	DGS30TO10_Log	Log of Yield Curve, 30 and 10 Year Treasury (DGS30-DGS10)
7768	DGS30TO10_mva200	Yield Curve, 30 and 10 Year Treasury (DGS30-DGS10) 200 Day MA
7775	DGS10TO1_SmoothDer	Derivative of Smoothed Yield Curve, 10 and 1 Year Treasury (DGS10-DGS1)
7776	DGS10TO1_Log	Log of Yield Curve, 10 and 1 Year Treasury (DGS10-DGS1)
7785	DGS10TO2_SmoothDer	Derivative of Smoothed Yield Curve, 10 and 2 Year Treasury (DGS10-DGS2)
7786	DGS10TO2_Log	Log of Yield Curve, 10 and 2 Year Treasury (DGS10-DGS2)
7795	DGS10TOTB3MS_SmoothDer	Derivative of Smoothed Yield Curve, 10 and 3 Month Treasury (DGS10-TB3MS)
7796	DGS10TOTB3MS_Log	Log of Yield Curve, 10 and 3 Month Treasury (DGS10-TB3MS)
7805	DGS10TODTB3_SmoothDer	Derivative of Smoothed Yield Curve, 10 and 3 Month Treasury (DGS10-DTB3)
7806	DGS10TODTB3_Log	Log of Yield Curve, 10 and 3 Month Treasury (DGS10-DTB3)
7813	DGS10ByAAA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) AAA ratio to 10 year treasury (AAA/DGS10)
7828	LNU03000000BYPOPTHM_mva200	Unemployment level (NSA) / Population 200 Day MA
7830	UNEMPLOYBYPOPTHM_YoY	Unemployment level, seasonally adjusted / Population Year over Year
7833	UNEMPLOYBYPOPTHM_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Unemployment level, seasonally adjusted / Population
7835	UNEMPLOYBYPOPTHM_SmoothDer	Derivative of Smoothed Unemployment level, seasonally adjusted / Population
7836	UNEMPLOYBYPOPTHM_Log	Log of Unemployment level, seasonally adjusted / Population
7839	UNEMPLOYBYPOPTHM_mva050	Unemployment level, seasonally adjusted / Population 50 Day MA
7845	NPPTTLBYPOPTHM_SmoothDer	Derivative of Smoothed ADP Private Employment / Population
7862	CHRISCMEHG1.by.PPIACO_YoY5	Copper, $/lb, Normalized by commodities producer price index 5 Year over 5 Year
7863	CHRISCMEHG1.by.PPIACO_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Copper, $/lb, Normalized by commodities producer price index
7866	CHRISCMEHG1.by.PPIACO_Log	Log of Copper, $/lb, Normalized by commodities producer price index
7867	CHRISCMEHG1.by.PPIACO_mva365	Copper, $/lb, Normalized by commodities producer price index 365 Day MA
7868	CHRISCMEHG1.by.PPIACO_mva200	Copper, $/lb, Normalized by commodities producer price index 200 Day MA
7869	CHRISCMEHG1.by.PPIACO_mva050	Copper, $/lb, Normalized by commodities producer price index 50 Day MA
7885	DCOILBRENTEU.by.PPIACO_SmoothDer	Derivative of Smoothed Crude Oil - Brent, $/bbl, Normalized by producer price index c.o.
7895	DCOILWTICO.by.PPIACO_SmoothDer	Derivative of Smoothed Crude Oil - WTI, $/bbl, Normalized by producer price index c.o.
7896	DCOILWTICO.by.PPIACO_Log	Log of Crude Oil - WTI, $/bbl, Normalized by producer price index c.o.
7907	LBMAGOLD.USD_PM.by.PPIACO_mva365	Gold, USD PM/Troy Ounce, Normalized by commodities producer price index 365 Day MA
7908	LBMAGOLD.USD_PM.by.PPIACO_mva200	Gold, USD PM/Troy Ounce, Normalized by commodities producer price index 200 Day MA
7915	LBMAGOLD.USD_PM.by.CPIAUCSL_SmoothDer	Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by consumer price index
7918	LBMAGOLD.USD_PM.by.CPIAUCSL_mva200	Gold, USD/Troy OUnce, Normalized by consumer price index 200 Day MA
7925	LBMAGOLD.USD_PM.by.GDP_SmoothDer	Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by GDP
7928	LBMAGOLD.USD_PM.by.GDP_mva200	Gold, USD/Troy OUnce, Normalized by GDP 200 Day MA
7936	GDP.by.GDPDEF_Log	Log of Nominal GDP Normalized by GDP def
7937	GDP.by.GDPDEF_mva365	Nominal GDP Normalized by GDP def 365 Day MA
7938	GDP.by.GDPDEF_mva200	Nominal GDP Normalized by GDP def 200 Day MA
7939	GDP.by.GDPDEF_mva050	Nominal GDP Normalized by GDP def 50 Day MA
7945	GSG.Close.by.GDPDEF_SmoothDer	Derivative of Smoothed GSCI Commodity-Indexed Trust, Normalized by GDP def
7955	GSG.Close.by.GSPC.Close_SmoothDer	Derivative of Smoothed GSCI Commodity-Indexed Trust, Normalized by S&P 500
7967	GDPBYPOPTHM_mva365	GDP/Population 365 Day MA
7968	GDPBYPOPTHM_mva200	GDP/Population 200 Day MA
7977	GDPBYCPIAUCSL_mva365	GDP divided by CPI 365 Day MA
7993	GSPC.CloseBYMDY.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365) GSPC by MDY
7995	GSPC.CloseBYMDY.Close_SmoothDer	Derivative of Smoothed GSPC by MDY
7999	GSPC.CloseBYMDY.Close_mva050	GSPC by MDY 50 Day MA
8003	QQQ.CloseBYMDY.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365) QQQ by MDY
8005	QQQ.CloseBYMDY.Close_SmoothDer	Derivative of Smoothed QQQ by MDY
8008	QQQ.CloseBYMDY.Close_mva200	QQQ by MDY 200 Day MA
8009	QQQ.CloseBYMDY.Close_mva050	QQQ by MDY 50 Day MA
8016	GSPC.DailySwing_Log	Log of S&P 500 (^GSPC) Daily Swing: (High - Low) / Open
8023	GSPC.Open.by.GDPDEF_Smooth	Savitsky-Golay Smoothed (p=3, n=365) S&P 500 (^GSPC) Open divided by GDP deflator
8025	GSPC.Open.by.GDPDEF_SmoothDer	Derivative of Smoothed S&P 500 (^GSPC) Open divided by GDP deflator
8028	GSPC.Open.by.GDPDEF_mva200	S&P 500 (^GSPC) Open divided by GDP deflator 200 Day MA
8029	GSPC.Open.by.GDPDEF_mva050	S&P 500 (^GSPC) Open divided by GDP deflator 50 Day MA
8033	GSPC.Close.by.GDPDEF_Smooth	Savitsky-Golay Smoothed (p=3, n=365) S&P 500 (^GSPC) Close divided by GDP deflator
8035	GSPC.Close.by.GDPDEF_SmoothDer	Derivative of Smoothed S&P 500 (^GSPC) Close divided by GDP deflator
8038	GSPC.Close.by.GDPDEF_mva200	S&P 500 (^GSPC) Close divided by GDP deflator 200 Day MA
8039	GSPC.Close.by.GDPDEF_mva050	S&P 500 (^GSPC) Close divided by GDP deflator 50 Day MA
8045	HNFSUSNSA.minus.HSN1FNSA_SmoothDer	Derivative of Smoothed Houses for sale - houses sold
8050	MSPUS.times.HOUST_YoY	New privately owned units start times median price Year over Year
8053	MSPUS.times.HOUST_Smooth	Savitsky-Golay Smoothed (p=3, n=365) New privately owned units start times median price
8055	MSPUS.times.HOUST_SmoothDer	Derivative of Smoothed New privately owned units start times median price
8056	MSPUS.times.HOUST_Log	Log of New privately owned units start times median price
8059	MSPUS.times.HOUST_mva050	New privately owned units start times median price 50 Day MA
8060	HOUST.div.POPTHM_YoY	Housing starts divided by U.S. population Year over Year
8063	HOUST.div.POPTHM_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Housing starts divided by U.S. population
8065	HOUST.div.POPTHM_SmoothDer	Derivative of Smoothed Housing starts divided by U.S. population
8066	HOUST.div.POPTHM_Log	Log of Housing starts divided by U.S. population
8068	HOUST.div.POPTHM_mva200	Housing starts divided by U.S. population 200 Day MA
8069	HOUST.div.POPTHM_mva050	Housing starts divided by U.S. population 50 Day MA
8075	MSPUS.times.HNFSUSNSA_SmoothDer	Derivative of Smoothed New privately owned 1-family units for sale times median price
8083	MSPUS.times.HSN1FNSA.plusEXHOSLUSM495S_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Median home price times new and existing houses sold
8085	MSPUS.times.HSN1FNSA.plusEXHOSLUSM495S_SmoothDer	Derivative of Smoothed Median home price times new and existing houses sold
8090	MSPUS.times.HSN1FNSA.plusEXHOSLUSM495S.by.GDP_YoY	New and existing home sales volume Year over Year
8093	MSPUS.times.HSN1FNSA.plusEXHOSLUSM495S.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) New and existing home sales volume
8095	MSPUS.times.HSN1FNSA.plusEXHOSLUSM495S.by.GDP_SmoothDer	Derivative of Smoothed New and existing home sales volume
8106	GSPC.Open_mva050_mva200	S&P 500 50 SMA - 200 SMA
8107	GSPC.Open_mva050_mva200_sig	Sell Signal S&P 500 50 SMA - 200 SMA
8108	MULTPLSP500PERATIOMONTH_Mean	S&P 500 TTM P/E Average (Excludes Values Greater Than 50)

Equities

S&P 500 SMA Trends

Take a look at recent activity in the equities market. This section looks at the S&P 500 close value to the 50 day and 200 day simple moving average (SMA).

This is a longer view for the S&P 500 trend.

Equity indexes normalized by GDP

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

The last two years compare favorably with the period around the late 1950’s. Need to dig into this one.

datay <- "GSPC.Close"
ylim <- c(2000, d.GSPC.max)
my.data <- plotSimilarPeriods(df.data, dfRecession, df.symbols, datay, ylim, i.window = 60)
my.data[[1]]

Look at how the different segments of the market move

datay <- "GSPC.CloseBYMDY.Close_YoY"
ylim <- c(-50, 75)
dtStart = as.Date('1980-01-01')
plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)

datay <- "GSPC.CloseBYMDY.Close"
ylim <- c(0, 20)
dtStart = as.Date('1980-01-01')
plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)

S&P 500 Normalized moving average

Look at moving average relationship by dividing the S&P 500 open price by the 200 day SMA.

datay <- "GSPC.Open_mva200_Norm"
ylim <- c(50, 125)
dt.start = as.Date('2008-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Crossovers

Look at the 50 DMA versus 200 DMA, often used as a technical indicator of market direction.

datay <- "GSPC.Open_mva050_mva200"
ylim <- c(-300, 300)
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStartBackTest)

datay <- "GSPC.Open_mva050_mva200_sig "
ylim <- c(0.0, 1.0)
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStartBackTest)

S&P 500 TTM P/E

Take a look at some of the earnings trends from SilverBlatt’s sheet.

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## New names:
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## * ...

Take a longer look back at as-reported and operating earnings

Market prices can out-run earnings so take a look at price to earnings.

Focus on some of the more recent activity

S&P 500 Sales

datay <- "MULTPLSP500SALESQUARTER"
ylim <- c(500, 2000)
dt.start <- as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

datay <- "MULTPLSP500SALESQUARTER"
ylim <- c(500, 2000)
dt.start = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Unit Profits

The series peaks in the middle of a bull market.

S&P 500 dividends

12-month real dividend per share inflation adjusted November, 2018 dollars. Data courtesy Standard & Poor’s and Robert Shiller.

https://www.quandl.com/data/MULTPL/SP500_DIV_MONTH-S-P-500-Dividend-by-Month

Evaluate year over year dividend growth.

Real value dividend growth.

datay <- "MULTPLSP500DIVMONTH_YoY"
ylim <- c(-40, 20)
dtStart = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

S&P 500 dividend yield (12 month dividend per share)/price. Yields following September 2018 (including the current yield) are estimated based on 12 month dividends through September 2018, as reported by S&P. Sources: Standard & Poor’s for current S&P 500 Dividend Yield. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Dividend Yields.

https://www.quandl.com/data/MULTPL/SP500_DIV_YIELD_MONTH-S-P-500-Dividend-Yield-by-Month

datay <- "MULTPLSP500DIVYIELDMONTH"
ylim <- c(0, 12)
dtStart = as.Date('1950-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

datay <- "MULTPLSP500DIVYIELDMONTH"
ylim <- c(1, 4)
dtStart = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

S&P 500 Volume

The log of the S&P volume has some interesting patterns, but nothing that seems to help with a recession indicator.

That is one spiky data series. Not sure there is a lot to help us here.

Russell 2000

Take a look at recent activity in the small cap market.

S&P 500 to Rusell 2000

Thirty day movement

Correlation

## Warning in max.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to max;
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## Warning in min.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to min;
## returning Inf

S&P 500 to MDY (Mid-cap) 2000 Correlation

datay1 <- "RLG.Open"
ylim1 <- c(0, 2500)

datay2 <- "MDY.Open"
ylim2 <- c(0, 500)

dtStart <- as.Date("1jan2003","%d%b%Y")

w <- 30
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

## Warning in max.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to max;
## returning -Inf

## Warning in min.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to min;
## returning Inf

Dividend Stocks

This is an interesting series, they should perform better through the recessions. Unfortunately they are short lived so there is not much data so this is more of a place holder for now.

datay <- "NOBL.Open"
ylim <- c(40, 110)
dt.start <- as.Date('2014-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Margin and option data

NYSE Margin Debt

Taking a look at margin debt. NYXDATA stopped providing NYSE margin debt data on Dec 2017. Data is available from FINRA, but it includes more accounts than the data did for NYXdata. I stitched togeter the data sets: data after Jan 2010 include NYSE+Others, data prior is just NYSE account data scaled up to match the FINRA data.

It tends to creep up when there is a frenzy in the stock market.

datay <- "FINRAMarginDebt_Log"
ylim <- c(5, 15)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Take a close look at recent activity

Sometimes it is more helpful to view year over year growth.

More near-term trend.

Take a look at some of the correlations

datay1 <- "FINRAMarginDebt_YoY"
ylim1 <- c(-100, 100)

datay2 <- "GSPC.Close_YoY"
ylim2 <- c(-100, 100)

dtStart <- as.Date("1jan1995","%d%b%Y")

w <- 90
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

Comparison to the Russell 2000

datay1 <- "FINRAMarginDebt_YoY"
ylim1 <- c(-100, 100)

datay2 <- "RLG.Close_YoY"
ylim2 <- c(-100, 100)

dtStart <- as.Date("1jan1995","%d%b%Y")

w <- 90
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

OCC Options Volumes

See what is happening with the options volumes for equities. (From: https://www.theocc.com/webapps/historical-volume-query)

Looks like options on non-equity co-occurs with peaks/troughs?.

Market Volatility

Take a look at some of the indications of market volatility

CBOE VIX

As markets become complacent (low VIX) and high values, peaks often occur.

Compare the VIX to some of the ETF’s out there.

There

Not much predictive in VIX, take a quick look at the smoothed derivative.

S&P Daily Swings

Daily changes in the S&P should correlate well with the VIX.

More of a correlating series than a predictor.

Employment and payrolls

Unemployment rates

Unemployment rates will probably be useful, let’s take a look at the U-3. The data is a little noisy so there is also a smoothed version plotted. There seems to be a relationship between the unemployment rate and the recessions, but it could be a lagging indicator. This will be explored a little bit more later.

Suggested by Charlie and a Wealthian video the 12 month-MA might be helpful to look at.

Looking at the unemployment rate, the eye is drawn to the rise and fall of the data, this suggests that the derivative might be helpful as well. The figure below shows the results, using a Savitzky-Golay FIR filter. It looks like the unemployment rate peaks in the middel of the recession. That peak might be a good buy signal.

Continuing Claims

A good measure of how much unemployment is growing.

Continued claims, also referred to as insured unemployment, is the number of people who have already filed an initial claim and who have experienced a week of unemployment and then filed a continued claim to claim benefits for that week of unemployment. Continued claims data are based on the week of unemployment, not the week when the initial claim was filed

https://fred.stlouisfed.org/series/CCNSA

A good measure of how much unemployment is growing

Initial Claims

A good measure of how much unemployment is growing.

An initial claim is a claim filed by an unemployed individual after a separation from an employer. The claim requests a determination of basic eligibility for the Unemployment Insurance program.

https://fred.stlouisfed.org/series/ICSA

Unemployment rates, year-over-year

Both the headline unemployment and U-6 number changes are similar. During the upswing on the cycle it does look like the headline number falls faster than U-6

The second derivative of the unemployment rate does have zero crossings near the middle point of a recession. This would make it a helpful buy signal for the trading strategy.

Unemployment rates, similar periods

Historically the last two years of record low unemployment appear most similar to the 1971-1973 time frame. Just before inflation took off.

Unemployment rates, U-6 and headline number.

Let’s also take a look at the total unemployed, U-6. It continues to fall as the headline number stabilizes as people return to the work force. An indicator the cycle is beginning to top out.

Difference between U6 and U3 to see how close the economy is getting to full employment.

Unemployment and market bottoms

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Initial jobless claims

We will also take a look at initial jobless claims, this should start to rise just before the unemployment rate.

It looks like the jobless claim tend to peak more towards the end of the recession. It does not seem to be as strong of a sell indicator as the U-3 rate.

Jobless claims have a seasonal component to them. One way to reduce this effect is to calculate year over year growth. That helps some, the peaks seem to be more closely aligned with the middle to end of recessions.

Take a closer look at recent data

## Warning: Removed 1 rows containing missing values (geom_text).

## Warning: Removed 1 rows containing missing values (geom_hline).

Take a look at the percentage of the population looking for work

A bit more recent trend

Unemployment Level

ADP data here. comes out before the official numbers.

Look at the year-over-year change in ADP.

ADP data divided by the population

Payrolls

Look at the BLS data on payrolls. Check the NSA series, then we will look at YoY data.

Hours worked

Sparked by an article at Mises (https://mises.org/wire/how-alexandria-ocasio-cortez-misunderstands-american-poverty), take a look at average weekly hours

The time series is pretty lumpy, plot the YoY change

A more recent look at average weekly hours of production

Industrial Production

Industrial production is also known to fall during an economic downturm, let’s take a look at some of the data from the FRED on industrial production. It does seem to peak prior to a recession so let’s smooth and look at the derivative as it might be a good indicator as well.

Industrial production over the last ten years or so

The derivative isn’t bad, but it sometimes crosses zeros well into a recession. That is less helpful as either a buy or sell indicator. A better measure might year over year (YoY) change.

The year over year change has a similar appearance. The low values at the beginning make the year over year values larger than the more recent values. Seems like it will rank low a reliable indicator.

datay1 <- "INDPRO_YoY"
ylim1 <- c(-20, 12)

datay2 <- "GSPC.Close_YoY"
ylim2 <- c(-100, 50)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Retail Sales

Retail sales, aggregate

Retail sales also change during recession. As the plot below shows, it seems to follow the trend of industrial production. It might be too strongly correlated to add much to the model. The will be examined in the correlation section.

The derivative of retail sales is a little more erratic than is was the industrial products. Looks like it might be helpful to include in the model as well.

Retail sales, aggregate year-over-year

Take a look at year-over-year changes

Retail sales and unemployment correlations

Let’s see how that looks on year over year basis. Interesting to compare to unemployment rates there appears to a correlation over the long term.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

There is some similarity. The rolling correlation shows the inverse relationship prior to a recession.

datay1 <- "RSALESAGG_YoY"
ylim1 <- c(-12.5, 12.5)

datay2 <- "UNEMPLOY_YoY"
ylim2 <- c(-30, 150)

dtStart <- as.Date("1jan1970","%d%b%Y")

w <- 180
corrName <- calcRollingCorr(dfRecession,df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Retail sales correlation and industrial production

Industrial production and retail sales look very similar so the plot below shows the 360 correlation. The corerlation does tend to fall around a recession, although 2008 was so bad that they both fell together. Not sure if it is that useful.

datay1 <- "INDPRO"
ylim1 <- c(40, 125)

datay2 <- "RSALESAGG"
ylim2 <- c(100000, 200000)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 60
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

It is interesting to see the strong correlation; however, I suspect this is due to more to the shape of the trends. How do the YoY correlations look? They are a little less correlated, probably better to use in the machine learning later.

datay1 <- "INDPRO_YoY"
ylim1 <- c(-20, 20)

datay2 <- "RSALESAGG_YoY"
ylim2 <- c(-20, 20)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Advance Retail Sales

This is an advanced estimate of the retail sales value.

Also take a look at year over year

Retail sales and the labor market

Income

Real Personal Income

Real Personal Income (Excluding Transfer, Annual)

During a recession real personal income falls. In the plot the peaks can be seen prior to each recession.

datay <- "W875RX1"
ylim <- c(3000, 15000)
plotSingleQuickModern(datay, ylim)

The features we are interested in are the peaks and valleys so we’ll use the derivative to get to those. Interesting, there is usually a first zero crossing before a recession and a second during or just after the recession.

Real personal income might have some seasonal variance, but it seems the year over year change tells the same story.

Price and cost measures

This section shows price and cost measures.

Two commonly used indexes are the CPI (consumer price index) and PPI (producer price index). CPI tries to show final prices paid for goods and services by urban U.S. consumers. This index includes sales tax and imports. The PPI attempts to reflect the prices paid at all stages of production, including goods and services purchases as inputs as well as goods and services purchased by consumers from retail and producer sellers. The PPI does not include imports or sales tax. The CPI reflects all rebates and financing plans wherease the PPI reflects only those rebate and financing plans provided by the producer. For example if an automotive manufacturer offers a rebate of $500 and the dealer offers an additional rebate of $500 then the PPI would reflect only the automotive manufacturer rebate, but the CPI would reflect both rebates.

Sources; https://www.bls.gov/opub/hom/pdf/cpihom.pdf and https://www.bls.gov/opub/hom/pdf/ppi-20111028.pdf.

Consumer price index

What does CPI look like?

datay <- "CPIAUCSL"
ylim <- c(0, 300)
plotSingleQuickModern(datay, ylim)

Check out the YoY growth

datay <- "CPIAUCSL_YoY"
ylim <- c(-2, 15)
plotSingleQuickModern(datay, ylim)

CPI to PPI

Suggested by Charlie, it can be helpful to look at the relationship between producer prices and consumer prices.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Producer Price Index (Commodities)

Commodities

Basket

Take a look at some trends of baskets of commodities.

This plot examines commodity performance relative to the GDP deflator

Crude oil

Look at a trend of West Texas Intermediate (WTI)

This is ticker data from yahoo

Take a look at both WTI and Brent crude.

Real price of crude using producer price index for commodities

Gold

As risks increase investors often flock to safe haven assets like gold. An up-tick in prices can indicate investor uncertainty. This can be seen in the nominal price plot around 1980 and again in 2007.

This plots out the real price of gold by two different deflators. PPI corrected price is a little higher, to be expected since CPI also includes the effects of sales tax and imports. The spike in 1980 is especially pronounced in this series.

See how nominal and real prices look year over year. From the long-term view seems like there is little difference in the three series. Although not shown, even over the near-term there is little difference in the series.

See how gold correlates with the VIX. Both gold and VIX should respond to investor axiety, but it doesn’t look like it correlates very well.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 242 rows containing non-finite values (stat_smooth).

Copper

Dr. Copper has a reputation as an indicator of economic malaise, but it does not seem to have much of a correlation with the recessions. The series below is from CME via Quandl. It has a lot of data so I am also looking at the smoothed version.

Copper is one of the commodities in the PPI so it is a bit of a proxy for how copper is doing relative to the basket of commodities.

The change in prices, year over year, do generally peak prior to a recession. The time and shape of this peak varies, but it still might be helpful. A couple of the large troughs do seem to correlate with the end of the recession. Likely this is because industrial production has also fallen.

There is some correlation between copper and the smooth recession initiator, especially at the end of the recession.

Might be easier to see correlation in a dot plot format.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 342 rows containing non-finite values (stat_smooth).

This is a legacy series from FRED. It has not been updated in a couple of years so I am assuming it will go away.

Oil Services

Amazing events in the first half of 2020, take a look at those

See how the players are doing

Federal Reserve

The federal reserve has an impact on the economy, here are some data series relating to that.

Little bit closer

datay <- "WALCL"
ylim <- c(0, 10000)
dtStart = as.Date('2003-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Federal Reserve Reverse Repo Agreements

Compare liabilities to reverse repo trends

Take a look at more recent trends

Spiky, might be easier to look at year-over-year

Normalized by GDP

datay <- "WLRRAL.by.GDP"
ylim <- c(0, 4)
dtStart = as.Date('2003-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Overnight Bank Funding Rate

“The overnight bank funding rate is calculated using federal funds transactions and certain Eurodollar transactions. The federal funds market consists of domestic unsecured borrowings in U.S. dollars by depository institutions from other depository institutions and certain other entities, primarily government-sponsored enterprises, while the Eurodollar market consists of unsecured U.S. dollar deposits held at banks or bank branches outside of the United States. U.S.-based banks can also take Eurodollar deposits domestically through international banking facilities (IBFs). The overnight bank funding rate (OBFR) is calculated as a volume-weighted median of overnight federal funds transactions and Eurodollar transactions reported in the FR 2420 Report of Selected Money Market Rates. Volume-weighted median is the rate associated with transactions at the 50th percentile of transaction volume. Specifically, the volume-weighted median rate is calculated by ordering the transactions from lowest to highest rate, taking the cumulative sum of volumes of these transactions, and identifying the rate associated with the trades at the 50th percentile of dollar volume. The published rates are the volume-weighted median transacted rate, rounded to the nearest basis point.” https://www.newyorkfed.org/markets/obfrinfo.

Secured Overnight Financing Rate

“The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of borrowing cash overnight collateralized by Treasury securities. The SOFR includes all trades in the Broad General Collateral Rate plus bilateral Treasury repurchase agreement (repo) transactions cleared through the Delivery-versus-Payment (DVP) service offered by the Fixed Income Clearing Corporation (FICC), which is filtered to remove a portion of transactions considered “specials” " https://apps.newyorkfed.org/markets/autorates/sofr

Take a look at the variation (99th - 1st percentile)

Reserve Balances with Federal Reserve Banks

Hard to get a sense of these series in the absolute. Take a look relative to GDP.

By double entry book-keeping reserves+loans (assets) = deposit (liabilities). Does that really work?

Correlation Between Reserves and Total Loans

As reserves increase there should be less lending. That correlation generally holds.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Did the reserve balances increase after the 2016 and 2018 drops? Not in the same way. There are some relationships between the equities market and the reserves though.

Explicitly correlate reserve balances and total loans. It is a weak and noisy correlation.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 990 rows containing non-finite values (stat_smooth).

Interest on excess reserves

Monetary Base

Currency trend, base

This used to trend along with GDP. It doesn’t anymore.

Money supplies

Basic currency trend (currency component of M1)

datay <- "WCURRNS_YoY"
dtStart = as.Date('1980-01-01')
ylim <- c(0, 17)
myplot <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)
myplot

datay <- "WCURRNS_YoY"
dtStart = as.Date('2000-01-01')
ylim <- c(0, 20)
myplot <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)
myplot

The rate of change of money supply could be an indicator of a recession. Let’s see how that compares.

Intervention in the repo market

The federal reserve provides liquidity to the repo market, summary of that action

European central bank

The European central band (ECB) has taken a different path compared to the US Federal Reserve bank.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Federal Debt

The government is a big driver of the economy, let’s see what it is doing in the debt markets.

datay <- "GFDEBTN"
ylim <- c(0, 35000000)
plotSingleQuick(dfRecession, df.data, datay, ylim)

datay <- "GFDEBTN_Log"
ylim <- c(12, 18)
plotSingleQuick(dfRecession, df.data, datay, ylim)

datay <- "GFDEBTN_YoY"
ylim <- c(-10, 25)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Federal debt as percent GDP

datay <- "GFDEGDQ188S"
ylim <- c(30, 150)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Federal deficit as percent GDP

datay <- "FYFSGDA188S"
ylim <- c(-30, 5)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Charlie Hatch has a nice format of deficit versus debt:

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Nonfinancial Corporate Business Debt

What about Nonfinancial corporate business and debt securities? Hopefully this doesn’t follow the business loan trends.

That is crazy steep. Time for a log format, see if that brings out the peaks and troughs. That’s a litte better, it looks like there might be a change in slope prior to the recessions.

The derivative doesn’t seem to be much help. There is not much correlation between the zero crossings and the NEBR recessions.

Debt cycle

This analysis roughly follows the ideas in Big Debt Crises book by Ray Dalio.

Total loans

One business cycle theory describes recessions as a market adjustment to mis-allocated assets, often fueled by an credit expansion. That makes the volume of loans an interesting feature to look at. In the presentation of data it looks like the great recession had the largest impact.

Plotting the year over year growth rate helps pull out those small changes in the early years in the data. Peaks can be seen prior to most recessions.

Zoom in to the last couple of decades

As long term interest rates rise, loans should start to tick down. To check this, the total loans and 10 to 1 year spreads are plotted. This is generally the trend observed.

There is a good correlation between these two variables. This next section plots that correction explicitly.

Total loans as percent of GDP

This is the total loans. I think the picture is too broad to point to a specific sector of the economy. The debt burden assumes interest rates are tied to the 10-year treasury: (TOTLNNSA * DGS10) / 100

Commercial and industral loans

Business loans should slow before the recession (a contraction in credit as rates rise).

Commercial and industrial loans as percent of GDP and and income

Look at business debt normalized by GDP over the entire time series. This ratio often peaks at the mid-point of a recession.

https://www.wsj.com/articles/this-isnt-your-fathers-corporate-bond-market-11590574555

“Bonds are behaving more like bank debt, which tends to remain stable or even increase at the onset of recessions, as lenders keep distressed clients afloat—and only later turn off the taps. This was confirmed by a recent report from the Bank for International Settlements. It also found a tight link between this lending cycle and the “real” economy’s booms and busts."

I assume that interest is related to the 10-year treasure: (TOTCINSA * DGS10) / 100

Farm loans

See how the farming sector is fairing.

Real estate loans

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

This gives a big picture, but makes it hard to connect the loans with the income needed to cover those loans. In the next section, loans will be broken up by commercial and residential.

Real Estate (Residential)

In absolute terms the mortgages have increased, but it does not appear to be out of line with the overall economy.

Normalized by GDP it is easier to see the peak in 2008 and that loan levels appear reasonable at the commercial banks. I updated this plot to include the estimated single-family home sales volume to give a sense of percentage of home sales that are cash.

Maybe the GSE’s are making loans. Take a look at the total mortgages from Z.1 as a percentage of GDP. That does not look too far off trend (ignoring that peak in 2008).

I am assuming that personal income is paying for the mortgages.

Real estate (residential) as percent of GDP and and income

## Warning: Removed 1 rows containing missing values (geom_text).

How do the number of starts compare to population?

Consumer loans

Focusing on the consumer sector the growth in debt and incomes can be directly compared. Personal income, as a percent of GDP, remains nearly constant. It is not uncommon for the personal income to rise prior to a recession. Likely this reflect increasing asset prices and market returns. Also interesting to see the loans pick up after interest rates dropped in 1982.

Consumer loans as percent of GDP and and income

Take a closer look since the 2008 recession. Looks like loans are starting to slow as the interest burden rises and incomes remain stable. There are some anomolies in the A065RC1A027NBEA data series because it only updates onces a year. the PI series updates once a month but is noisier and seasonally adjusted. It also shows incomes rising in the middle of the 2008 recession, which doesn’t seem to be accurate.

## Warning: Removed 1 rows containing missing values (geom_text).
## Removed 1 rows containing missing values (geom_text).

## Warning: Removed 1 rows containing missing values (geom_hline).

Repo market

This market went through some stress in 2008, it is happening again so setup some plots to watch it.

Nonfincial corporate business security repo asset level

Bonds

T-Bills and Yield Curve

Speaking of loans, interest rates also play into this. This analysis will focus on treasure bills. The 3-month is plotted below. The yield flattens before a recession as investors go long on bonds and short on equities.

datay <- "TB3MS"
datay.aux <- "DTB3"
ylim <- c(0, 20)
p1 <- plotSingleQuickModern(datay, ylim)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

datay <- "TB3MS"
datay.aux <- "DTB3"
ylim <- c(0, 6.0)
dtStart = as.Date('2017-01-01')
p1 <- plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

# {r bond3monthlibor, echo=FALSE } # # datay <- "TB3MS" # datay_aux <- "USD1MTD156N" # ylim <- c(0, 12) # dtStart = as.Date('1985-01-01') # myPlot <- plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", # getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE) # myPlot <- myPlot + geom_line(data=df.data, aes_string(x="date", y=datay_aux, colour=shQuote(datay_aux)), na.rm = TRUE) # # myPlot # # Check out LIBOR and fed funds rate

The 1-year is plotted below. The yield flattens before a recession as investors go long on bonds and short on equities.

datay <- "DGS10"
datay.aux <- "TNX.Close"
ylim <- c(0, 20)
p1 <- plotSingleQuickModern(datay, ylim)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

Close in, the trend towards inversion be more easily seen. I am also comparing data from the CBOE as well as FRED.

Bond yields are a good proxy for interest rates. As rates rise the theory goes that loans should decrease (inverse correlation).

And a longer window

The yield curve (30 year bond rate minus the 10 year bond rate) may not be a good recession indicator, but a collapse is not good (https://blogs.wsj.com/moneybeat/2018/04/30/theres-more-than-one-part-of-the-yield-curve-getting-flatter/).

The yield curve (10 year bond rate minus the 1 year bond rate) seems to a good indicator of an oncoming recession. It could be a buy indicator by itself.

More recent data

Just the last 24 months or so.

Plot the 10 Year to 3 month over a few decades to see what the outling cases look like

The last two year compare favorably with the period around the 2015-2016 turndown, driven primarily by slowing of the Chinese GDP. Not a debt-driven cycle.

This plot format was suggested by a mises.org article (https://mises.org/wire/yield-curve-accordion-theory), but they only went back to 1988. The date seemed arbitrary so I went back further in time.

Take a look at more recent data

Try looking at a 1-year average of the above time series

High quality bonds

datay <- "AAA"
ylim <- c(1.5, 10)
dtStart = as.Date('1997-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High quality bonds to 10-year treasury

High quality bonds long-term trend.

datay <- "DGS10ByAAA"
ylim <- c(1, 6.0)
dtStart = as.Date('1967-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High quality bonds near-term trend.

datay <- "DGS10ByAAA"
ylim <- c(1, 6.0)
dtStart = as.Date('2007-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High yield spread

“This data represents the Option-Adjusted Spread (OAS) of the ICE BofAML US Corporate A Index, a subset of the ICE BofAML US Corporate Master Index tracking the performance of US dollar denominated investment grade rated corporate debt publicly issued in the US domestic market. This subset includes all securities with a given investment grade rating A. The ICE BofAML OASs are the calculated spreads between a computed OAS index of all bonds in a given rating category and a spot Treasury curve. An OAS index is constructed using each constituent bond‚Äôs OAS, weighted by market capitalization. When the last calendar day of the month takes place on the weekend, weekend observations will occur as a result of month ending accrued interest adjustments.”

• ICE Benchmark Administration Limited (IBA), ICE BofAML US Corporate A Option-Adjusted Spread [BAMLC0A3CA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BAMLC0A3CA, July 4, 2019.

datay <- "BAMLC0A3CA"
ylim <- c(0, 7)
dtStart = as.Date('1997-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Municipal bond market

Suggest by a WSJ article, change in volume for high-risk muni’s. Doesn’t look like there is much too it yet.

https://www.wsj.com/articles/risky-municipal-bonds-are-on-a-hot-streak-11558949401?mod=hp_lead_pos3

datay <- "HYMB.Close"
ylim <- c(40, 62)
dtStart = as.Date('2011-01-01')
p1 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

datay <- "HYMB.Volume"
ylim <- c(0, 1750000)
p1.vol <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )


datay <- "GSPC.Open"
datay_aux <- "GSPC.Close"
ylim <- c(1500, d.GSPC.max )
p2 <-
  plotSingle(
    dfRecession,
    df.data,
    "date",
    datay,
    getPlotTitle(df.symbols, datay),
    "Date",
    getPlotYLabel(df.symbols, datay),
    c(dtStart, Sys.Date()),
    ylim,
    TRUE
  )

p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )


grid.arrange(p1,
             p1.vol,
             p2,
             ncol = 1,
             top = "High Yield Muni's and S&P Price")

Total Loans and yield curve correlation

This relationship was suggest by Charlie and it is an interesting one. As the yield curve flattens (10-year and 1-year rates converge), total loans grow. The generalization is not always accurate, but it does fit.

## `geom_smooth()` using formula 'y ~ x'

I wanted to see how this looked compared to the 3 month

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 282 rows containing non-finite values (stat_smooth).

Consumer loans and yield curve correlation

Compared to business loans, consumer loans seem to have to response to the 10Y to 3M yield curve.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 311 rows containing non-finite values (stat_smooth).

Business loans and yield curve correlation

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 103 rows containing non-finite values (stat_smooth).

That’s pretty good correlation. Let’s see what the rolling correlation looks like.

datay1 <- "TOTLNNSA_YoY"
ylim1 <- c(-10, 20)

datay2 <- "DGS10TO1"
ylim2 <- c(-5, 10)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

datay1 <- "TOTLNNSA_YoY"
ylim1 <- c(-10, 20)

datay2 <- "DGS10TO1"
ylim2 <- c(-5, 10)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 720
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

One other items, let’s see how loans do versus the federal funds rate

## `geom_smooth()` using formula 'y ~ x'

Baker Hughes Rig Count

BEA Supplemental Estimates, Motor Vehicles

Definitions

Autos–all passenger cars, including station wagons.
Light trucks–trucks up to 14,000 pounds gross vehicle weight, including minivans and
sport utility vehicles. Prior to the 2003 Benchmark Revision light trucks were up to 10,000 pounds.
Heavy trucks–trucks more than 14,000 pounds gross vehicle weight.
Prior to the 2003 Benchmark Revision heavy trucks were more than 10,000 pounds.
Domestic sales–United States (U.S.) sales of vehicles assembled in the U.S., Canada, and Mexico.
Foreign sales–U.S. sales of vehicles produced elsewhere.
Domestic auto production–Autos assembled in the U.S.
Domestic auto inventories–U.S. inventories of vehicles assembled in the U.S., Canada, and Mexico.

TAble 6 - Light Vehicle and Total Vehicle Sales

Auto sales

A WSJ article suggested that auto sales might be a good indicator so bring that to the mix. It does have troughs that correlate with recessions

There might be some seasonal variance in the auto sales so lets take a look at the year over year. The data is pretty noisy, it probably will not make a very good indicator.

BEA Gross Domestic Product

Data in this section come from the Bureau of Economic Analysis.

Table 1.1.5. Gross Domestic Product

[Billions of dollars] Seasonally adjusted at annual rates

A191RC: Gross Domestic Product - Line 1

GDP numbers tend to lag so this series is truly an afterthought. But it does have some correlation with the recessions.

GDP does not reflect the capacity of the economy nor the efficiency. Shrinking capacity and lower prices at constant volumes would indicate improvements in effeciency/productivity which is good for the economy, but does not move the GDP upward.

Looks like the year over year change on the GDP should correlate well with unemployment.

Table 1.1.9. Implicit Price Deflators for Gross Domestic Product

[Index numbers, 2012=100] Seasonally adjusted

A191RD: Gross Domestic Product - Line 1

This is GDP price deflator series.

GDP normalized by CPI

Normalize GDP by CPI

Economic yield curve (GDP to 1-year treasury)

GDP versus the yield on the 1-year. This series was prompted by an article suggesting that the “economic yield curve” should be used to indicate a recession rather than an inverted yield curve. Less of indicator and more of concurrent confirmation of recession. Not sure why they would be related either.

Economic yield curve (GDP to 3-month treasury)

Same idea as above, but applied the 3-month treasury.This one has fewer false triggers, but is not as helpful as 10Y to 3M spread in predicting a recession.

A824RC: National defense Federal Gov’t Expenditures - Line 24

U.S. Bureau of Economic Analysis, Federal Government: National Defense Consumption Expenditures and Gross Investment [FDEFX], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FDEFX, April 6, 2021.

A825RC: Nondefense Federal Gov’t Expenditures - Line 25

U.S. Bureau of Economic Analysis, Federal Government: Nondefense Consumption Expenditures and Gross Investment [FNDEFX], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FNDEFX, April 6, 2021.

Table 6.16D. Corporate Profits by Industry

Select series from Table 6.16D

A051RC: Corporate profits with inventory and capital consumption adjustment

From BEA’s documentation (https://www.bea.gov/media/5671):

“BEA’s featured measure of corporate profits — profits from current production - provides a comprehensive and consistent economic measure of the income earned by all U.S. corporations. As such, it is unaffected by changes in tax laws, and it is adjusted for nonreported and misreported income. It excludes dividend income, capital gains and losses, and other financial flows and adjustments, such as deduction for “bad debt.” Thus, the NIPA measure of profits is a particularly useful analytical measure of the health of the corporate sector. For example, in contrast to other popular measures of corporate profits, the NIPA measure did not show the large run-up in profits during the late 1990s that was primarily attributable to capital gains.

Profits after tax with IVA and CCAdj is equal to corporate profits with IVA and CCAdj less taxes on corporate income. It provides an after-tax measure of profits from current production."

Data is Line 1 of Table 6.16D

A053RC: Corporate profits without inventory and capital consumption adjustment

Profits look a bit flat over the last several years in this series.

Table 2.6. Personal Income and Its Disposition, Monthly

Billions of dollars; months are seasonally adjusted at annual rates.

A065RC Personal Income - Line 1

BEA Account Code: A065RC

Personal income is the income that persons receive in return for their provision of labor, land, and capital used in current production and the net current transfer payments that they receive from business and from government.25 Personal income is equal to national income minus corporate profits with inventory valuation and capital consumption adjustments, taxes on production and imports less subsidies, contributions for government social insurance, net interest and miscellaneous payments on assets, business current transfer payments (net), current surplus of government enterprises, and wage accruals less disbursements, plus personal income receipts on assets and personal current transfer receipts. A Guide to the National Income and Product Accounts of the United States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Income [PI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PI, July 11, 2019.

DPCERC: Personal consumption expenditures (PCE) - Table 2.1, Line 29

BEA Account Code: DPCERC Personal consumption expenditures (PCE) is the primary measure of consumer spending on goods and services in the U.S. economy. 1 It accounts for about two-thirds of domestic final spending, and thus it is the primary engine that drives future economic growth. PCE shows how much of the income earned by households is being spent on current consumption as opposed to how much is being saved for future consumption. -https://www.bea.gov/system/files/2019-12/Chapter-5.pdf

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Consumption Expenditures [PCE], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCE, June 12, 2020

DPCERG: Personal consumption expenditures Price Index (PCEPI) - Table 2.1, Line 29

BEA Account Code: DPCERG The gross domestic product price index measures changes in prices paid for goods and services produced in the United States, including those exported to other countries. Prices of imports are excluded. The gross domestic product implicit price deflator, or GDP deflator, basically measures the same things and closely mirrors the GDP price index, although the two price measures are calculated differently. The GDP deflator is used by some firms to adjust payments in contracts.

The gross domestic purchases price index is BEA’s featured measure of inflation for the U.S. economy overall. It measures changes in prices paid by consumers, businesses, and governments in the United States, including the prices of the imports they buy.

BEA’s closely followed personal consumption expenditures price index, or PCE price index, is a narrower measure. It looks at the changing prices of goods and services purchased by consumers in the United States. It’s similar to the Bureau of Labor Statistics’ consumer price index for urban consumers. The two indexes, which have their own purposes and uses, are constructed differently, resulting in different inflation rates.

The PCE price index is known for capturing inflation (or deflation) across a wide range of consumer expenses and for reflecting changes in consumer behavior. For example, if the price of beef rises, shoppers may buy less beef and more chicken. Also, BEA revises previously published PCE data to reflect updated information or new methodology, providing consistency across decades of data that’s valuable for researchers. The PCE price index is used primarily for macroeconomic analysis and forecasting. -https://www.bea.gov/resources/learning-center/what-to-know-prices-inflation

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Consumption Expenditures: Chain-type Price Index [PCEPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCEPI, April 25, 2021.

A072RC: Personal Savings Rate - Line 35

Consumers tend to pull down their savings rates as unemployment decreases and market conditions improve. This series has tended to be unreliable due to the size of revisions during the comprehensive update carried out by the BEA. The last update on this series moved the rate from 4.2 to 6.7 percent.

(https://www.bloomberg.com/news/articles/2018-07-27/americans-have-been-saving-much-more-than-thought-new-data-show)

BEA Account Code: A072RC Personal saving as a percentage of disposable personal income (DPI), frequently referred to as “the personal saving rate,” is calculated as the ratio of personal saving to DPI. Personal saving is equal to personal income less personal outlays and personal taxes; it may generally be viewed as the portion of personal income that is used either to provide funds to capital markets or to invest in real assets such as residences.(https://www.bea.gov/national/pdf/all-chapters.pdf) A Guide to the National Income and Product Accounts of the United States (NIPA).

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Saving Rate [PSAVERT], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PSAVERT, July 9, 2019.

Take a closer look at the last decade

The relationship between personal savings and unemployment (U-3) can be better visualized with a scatter plot

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 195 rows containing non-finite values (stat_smooth).

The fit does not explain most of what is in the plot. Lets take a look at the rolling correlation.

datay1 <- "UNRATE"
ylim1 <- c(2, 12)

datay2 <- "PSAVERT"
ylim2 <- c(0, 35)

dtStart <- as.Date("1jan1985","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Personal savings to household net worth

A relationship between personal savings and household networth can be seen in a scatter plot. This was suggested by a WSJ article (https://blogs.wsj.com/dailyshot/2018/02/23/the-daily-shot-reasons-for-declining-u-s-household-savings-rate/).

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 1028 rows containing non-finite values (stat_smooth).

U.S. Census Bureau

U.S. International Trade in Goods and Services (FT900)

U.S. Bureau of Economic Analysis and U.S. Census Bureau, U.S. Imports of Goods by Customs Basis from China [IMPCH], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/IMPCH, October 5, 2019.

New Houses Sold and For Sale by Stage of Construction and Median Number of Months on Sales Market

Read an article suggesting that housing sales and sales growth could be useful. FRED only has new home data so start there.

datay <- "HSN1FNSA"
ylim <- c(0, 200)
dtStart = as.Date('1964-01-01')
p1 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "HNFSUSNSA"
ylim <- c(0, 600)
p2 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "HNFSUSNSA.minus.HSN1FNSA"
ylim <- c(0, 600)
p3 <-
  plotSingle(
    dfRecession,
    df.data,
    "date",
    datay,
    getPlotTitle(df.symbols, datay),
    "Date",
    getPlotYLabel(df.symbols, datay),
    c(dtStart, Sys.Date()),
    ylim,
    TRUE
  )

grid.arrange(p1,
             p2,
             p3,
             ncol = 1,
             top = "New Housing Sales")

New housing yoy

New Privately-Owned Housing Units Authorized in Permit-Issuing Places

As provided by the Census, start occurs when excavation begins for the footings or foundation of a building. All housing units in a multifamily building are defined as being started when this excavation begins. Beginning with data for September 1992, estimates of housing starts include units in structures being totally rebuilt on an existing foundation.

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, Housing Starts: Total: New Privately Owned Housing Units Started [HOUST], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/HOUST, June 13, 2020.

Take a look at privately owned starts

New Privately-Owned Houses Sold and For Sale

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, Median Sales Price of Houses Sold for the United States [MSPUS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/MSPUS, June 13, 2020.

Finally, take a look at starts times the median price

Durable Goods

Suggested Citation: U.S. Census Bureau, Manufacturers’ New Orders: Durable Goods [UMDMNO], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/UMDMNO, April 26, 2021.

Durable goods, not seasonally adjusted, divided by GDP

Durable goods, seasonally adjusted, divided by GDP

Federal reserve board H.8: Assets and Liabilities of Commercial Banks in the United States

Page 4: Not Seasonally adjusted, billions of dollars

Commercial and industrial loans, all commercial banks - Line 10

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

Suggested Citation: Board of Governors of the Federal Reserve System (US), Commercial and Industrial Loans, All Commercial Banks [BUSLOANS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BUSLOANS, July 11, 2019.

Taking a look at the difference in SA and NSA series. Seasonal adjustments do vary, but do not seem to be related to recessions.

The raw series is just too steep for any kind of machine learnine. This needs to be converted to log scale.

That’s a little better, let’s see what the smoothed derivative looks like.

That is odd…looks like this doesn’t cross zero unless we are getting close to, or into, a recession. The year over year tells about the same story. Might be a good indication of the end of a recession.

Consumer loans, all commercial banks - Line 20

Suggested Citation: Board of Governors of the Federal Reserve System (US), Consumer Loans, All Commercial Banks [CONSUMERNSA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CONSUMERNSA, July 11, 2019.

That spike in consumer loans is due to

“April 9, 2010 (Last revised September 23, 2011): As of the week ending March 31, 2010, domestically chartered banks and foreign-related institutions had consolidated onto their balance sheets the following assets and liabilities of off-balance-sheet vehicles, owing to the adoption of FASB’s Financial Accounting Statements No. 166 (FAS 166),”Accounting for Transfers of Financial Assets," and No. 167 (FAS 167), “Amendments to FASB Interpretation No. 46(R).”

This included a consumer loans, credit cards and other revolving plans change of $321.9B. That was a lot of off-balance-sheet bank assets.

Deposits, All Commercial Banks, all commercial banks - Line 34

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

Suggested Citation: Board of Governors of the Federal Reserve System (US), Deposits, All Commercial Banks [DPSACBW027SBOG], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/DPSACBW027SBOG, May 14, 2020.

Federal reserve board Z.1: Financial Accounts of the United States

From the FRED website (https://fred.stlouisfed.org/release?rid=52):

"The Financial Accounts (formerly known as the Flow of Funds accounts) are a set of financial accounts used to track the sources and uses of funds by sector. They are a component of a system of macroeconomic accounts including the National Income and Product accounts (NIPA) and balance of payments accounts, all of which serve as a comprehensive set of information on the economy’s performance.(1) Some important inferences that can be drawn from the Financial accounts are the financial strength of a given sector, new economic trends, changes in the composition of wealth, and development of new financial instruments over time.(1)

Sectors are compiled into three categories: households, nonfinancial businesses, and banks. The sources of funds for a sector are its internal funds (savings from income after consumption) and external funds (loans from banks and other financial intermediaries). (1) Funds for a given sector are used for its investments in physical and financial assets. Dividing sources and uses of funds into two categories helps the staff of the Federal Reserve System pay particular attention to external sources of funds and financial uses of funds.(2) One example is whether households are borrowing more from banks—or in other words, whether household debt is rising. Another example might be whether banks are using more of their funds to provide loans to consumers. Transactions within a sector are not shown in the accounts; however, transactions between sectors are.(2) Monitoring the external flows of funds provides insights into a sector’s health and the performance of the economy as a whole.

Data for the Financial accounts are compiled from a large number of reports and publications, including regulatory reports such as those submitted by banks, tax filings, and surveys conducted by the Federal Reserve System.(2) The Financial accounts are published quarterly as a set of tables in the Federal Reserve’s Z.1 statistical release.

1. Teplin, Albert M. “The U.S. Flow of Funds Accounts and Their Uses.” Federal Reserve Bulletin, July 2001; http://www.federalreserve.gov/pubs/bulletin/2001/0701lead.pdf.
2. Board of Governors of the Federal Reserve System. “Guide to the Flow of Funds Accounts.” 2000, http://www.federalreserve.gov/apps/fof/."

L.102 Nonfinancial Business

FL102051003.Q: Nonfinancial corporate business; security repurchase agreements; asset

Asset level of nonfinancial business security repo agreements. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL102051003&t=

L.214 Loans

FL894123005.Q: All sectors; total loans; liability

Sum of domestic financial sectors, all sectors, total mortgages, and households/non-profits. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL894123005&t=L.107&bc=L.107:FL793068005&suf=Q

FL793068005.Q: Domestic financial sectors; depository institution loans n.e.c.; asset

Sum of Monetary authority; depository institution loans n.e.c.; asset and Private depository institutions; depository institution loans n.e.c.; asset. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL793068005&t=L.214&suf=Q

FL893169005.Q: All sectors; other loans and advances; liability

Sum of finance, government, and chartered institutions asset levels. https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893169005&t=L.214&suf=Q

FL893065005.Q: All sectors; total mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065005&t=L.214&suf=Q

FL893065105.Q: All sectors; home mortgages; asset

https://www.federalreserve.gov/apps/fof/DisplayTable.aspx?t=L.214

FL893065405.Q: All sectors; multifamily residential mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065405&t=L.214&suf=Q

FL893065505.Q: All sectors; commercial mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065505&t=L.214&suf=Q

FL893065603.Q: All sectors; farm mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065603&t=L.214&suf=Q

FL153166000.Q: Households and nonprofit organizations; consumer credit; liability

federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL153166000&t=L.214&suf=Q

B.101 Balance Sheet of Households and Nonprofit Organizations

FL152000005.Q: Households and nonprofit organizations; total assets, Level

string.source ID: FL152000005.Q.

FL152090006.Q: Household Net Worth as Percentage of Disposable Personal Income

string.source ID: FL152090006.Q. Household networth tends to fall as a recession start.

Productivity Yield Curve

GDP versus productivity

Manufacturing output and employees

Not sure if these relates to a recession, but fascinating to see how output and employees change with time.

datay <- "OUTMS"
ylim <- c(60, 120)
dtStart = as.Date('1987-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "MANEMP"
ylim <- c(10000, 20000)
dtStart = as.Date('1948-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "PRS30006163"
ylim <- c(40, 120)
dtStart = as.Date('1986-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Shipping volumes might be helpful in determining state of the economy.

datay <- "FRGSHPUSM649NCIS"
ylim <- c(0.8, 1.4)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "FRGSHPUSM649NCIS_YoY"
ylim <- c(-30, 30)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Freight, loosely, moves inversely to the trade deficit.

datay <- "BOPGTB_YoY"
ylim <- c(-30, 30)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

World bank air transportation. Only updated annually so less usefull, but interesting reference to above.

datay <- "WWDIWLDISAIRGOODMTK1"
ylim <- c(0, 250000)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Gross private domestic investment

Spending most certainly tips down prior to a recession. The gross private domestic investment data series, plotted in log format below, show how private investment pulls back prior to recessions.

The change in direction is a little easier to see if the derivative is plotted, first YoY then the smoothed derivative

Velocity

Productivity

Frequency: Quarterly The Productivity and Costs release on August 7, 2003, will reflect the June 2003 benchmark revision to payroll employment. Since employment is now reported on a North American Industry Classification System (NAICS) basis, all of the historical data will be revised. Changes as a consequence of the move to NAICS should not be significant since this release carries data at high levels of aggregation.

Suggested Citation: U.S. Bureau of Labor Statistics, Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Employed Persons [OPHNFB], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/OPHNFB, December 24, 2022.

Date range to match census data

PMI

Industrial Production

This is a look at manufacturing industrial production. The yoY change should be a leading indicator of unemployment.

Housing

Take a look at housing starts. These can drop as rates rise.

Frequency: Monthly

As provided by the Census, start occurs when excavation begins for the footings or foundation of a building. All housing units in a multifamily building are defined as being started when this excavation begins. Beginning with data for September 1992, estimates of housing starts include units in structures being totally rebuilt on an existing foundation.

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, New Privately-Owned Housing Units Started: Total Units [HOUST], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/HOUST, December 24, 2022.

Housing starts, NSA

HOUST reports at annual rate, but HOUSTNSA just reports the monthly numbers. I scale up the NSA to the annual rate.

Units: Thousands of Units, Not Seasonally Adjusted

Frequency: Monthly

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, New Privately-Owned Housing Units Started: Total Units [HOUSTNSA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/HOUSTNSA, December 24, 2022.

Case-schiller price index

Population data

Many of the economic series can be better understood if normalized by population. Basic population and worker data from FRED.

Population to GDP

Look at GDP divided by CPI per person. It flattens and even dips a little prior to a recession. Might be worth looking at the derivative of this series.

That is worth a closer look

datay1 <- "GDPBYCPIAUCSLBYPOPTHM_SmoothDer"
ylim1 <- c(-5, 5)

datay2 <- "RecInit_Smooth"
ylim2 <- c(0, 1)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Correlation Study

Detailed correlations are explored above. Before concluding, let’s take a look at some overall correlation values to see if anything pops out.

Commodities

As mentioned above, copper, year over year, has some correlation with the recession initiation. It could be useful.

GDP Series

GDP, normalized first by CPI and then by population, looks like it migh correlate inversely with the recession indicators

Financials

Let’s see where we are so far. The correlation plot confirms some of the speculation above. The S&P 500 (GSPC.Open) is well correlated with industrial production (INDPRO), business loans (BUSLOANS), total loans (TOTLNNSA) , and nonfinancial corporate business debt (NCBDBIQ027S).

In this case, I want and indicator that rises prior to a recession. It looks like the unemployment rate (UNRATE), real personal income (W875RX1), and the yield curve (DGS10TO1) are all inversely correlated with the recession initiation indicator.

I thought the modified recession initiation would be a harder match, but there are quite a few correlated variables. Lets take a look at some of those in more detail

Complete list of symbols

Since it is tedious to do this one at a time, all the symbols were entered into a data frame, loaded, and aggregated together in a single xts object.

This is the complete list of symbol names and sources used in the project.